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GitHub - microsoft/BiomedParse: BiomedParse: A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities
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2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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</a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 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flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a 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data-analytics-event="{"location":"navbar","action":"collections","context":"open_source","tag":"link","label":"collections_link_open_source_navbar"}" href="https://github.com/collections"> Collections </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Enterprise <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> 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dir=\"auto\"\u003e\u003cstrong\u003eBiomedParse\u003c/strong\u003e\u003c/h1\u003e\u003ca id=\"user-content-biomedparse\" class=\"anchor\" aria-label=\"Permalink: BiomedParse\" href=\"#biomedparse\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository hosts the code and resources for the paper \u003cstrong\u003e\"A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities\"\u003c/strong\u003e (published in \u003ca href=\"https://aka.ms/biomedparse-paper\" rel=\"nofollow\"\u003e\u003cem\u003eNature Methods\u003c/em\u003e\u003c/a\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[\u003ca href=\"https://aka.ms/biomedparse-paper\" rel=\"nofollow\"\u003e\u003ccode\u003ePaper\u003c/code\u003e\u003c/a\u003e] [\u003ca href=\"https://microsoft.github.io/BiomedParse/\" rel=\"nofollow\"\u003e\u003ccode\u003eDemo\u003c/code\u003e\u003c/a\u003e] [\u003ca href=\"https://huggingface.co/microsoft/BiomedParse\" rel=\"nofollow\"\u003e\u003ccode\u003eModel\u003c/code\u003e\u003c/a\u003e] [\u003ca href=\"https://huggingface.co/datasets/microsoft/BiomedParseData\" rel=\"nofollow\"\u003e\u003ccode\u003eData\u003c/code\u003e\u003c/a\u003e] [\u003ca href=\"#Citation\"\u003e\u003ccode\u003eBibTeX\u003c/code\u003e\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBiomedParse\u003c/strong\u003e is designed for comprehensive biomedical image analysis. It offers a unified approach to perform \u003cstrong\u003esegmentation\u003c/strong\u003e, \u003cstrong\u003edetection\u003c/strong\u003e, and \u003cstrong\u003erecognition\u003c/strong\u003e across diverse biomedical imaging modalities. By consolidating these tasks, BiomedParse provides an efficient and flexible tool tailored for researchers and practitioners, facilitating the interpretation and analysis of complex biomedical data.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/microsoft/BiomedParse/blob/main/assets/readmes/biomedparse_prediction_examples.png\"\u003e\u003cimg src=\"/microsoft/BiomedParse/raw/main/assets/readmes/biomedparse_prediction_examples.png\" alt=\"Example Predictions\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNews\u003c/h2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-label=\"Permalink: News\" href=\"#news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJan. 9, 2025: Refined all object recognition script and added notebook with examples.\u003c/li\u003e\n\u003cli\u003eDec. 12, 2024: Uploaded extra datasets for finetuning on [\u003ca href=\"https://huggingface.co/datasets/microsoft/BiomedParseData\" rel=\"nofollow\"\u003e\u003ccode\u003eData\u003c/code\u003e\u003c/a\u003e]. Added random rotation feature for training.\u003c/li\u003e\n\u003cli\u003eDec. 5, 2024: The loading process of target_dist.json is optimized by automatic downloading from HuggingFace.\u003c/li\u003e\n\u003cli\u003eDec. 3, 2024: We added inference notebook examples in inference_example_RGB.ipynb and inference_example_NIFTI.ipynb\u003c/li\u003e\n\u003cli\u003eNov. 22, 2024: We added negative prediction p-value example in inference_example_DICOM.ipynb\u003c/li\u003e\n\u003cli\u003eNov. 18, 2024: BiomedParse is officially online in \u003ca href=\"https://aka.ms/biomedparse-paper\" rel=\"nofollow\"\u003e\u003cem\u003eNature Methods\u003c/em\u003e\u003c/a\u003e!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/microsoft/BiomedParse.git\"\u003e\u003cpre\u003egit clone https://github.com/microsoft/BiomedParse.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e[Notice] If inference_utils/target_dist.json is not cloned correctly, it will be automatically loaded from HuggingFace when needed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConda Environment Setup\u003c/h3\u003e\u003ca id=\"user-content-conda-environment-setup\" class=\"anchor\" aria-label=\"Permalink: Conda Environment Setup\" href=\"#conda-environment-setup\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOption 1: Directly build the conda environment\u003c/h4\u003e\u003ca id=\"user-content-option-1-directly-build-the-conda-environment\" class=\"anchor\" aria-label=\"Permalink: Option 1: Directly build the conda environment\" href=\"#option-1-directly-build-the-conda-environment\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUnder the project directory, run\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda env create -f environment.yml\"\u003e\u003cpre\u003econda env create -f environment.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOption 2: Create a new conda environment from scratch\u003c/h4\u003e\u003ca id=\"user-content-option-2-create-a-new-conda-environment-from-scratch\" class=\"anchor\" aria-label=\"Permalink: Option 2: Create a new conda environment from scratch\" href=\"#option-2-create-a-new-conda-environment-from-scratch\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create -n biomedparse python=3.9.19\nconda activate biomedparse\"\u003e\u003cpre\u003econda create -n biomedparse python=3.9.19\nconda activate biomedparse\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInstall Pytorch\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn case there is issue with detectron2 installation, make sure your pytorch version is compatible with CUDA version on your machine at \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ehttps://pytorch.org/\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInstall dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -r assets/requirements/requirements.txt\"\u003e\u003cpre\u003epip install -r assets/requirements/requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDataset\u003c/h2\u003e\u003ca id=\"user-content-dataset\" class=\"anchor\" aria-label=\"Permalink: Dataset\" href=\"#dataset\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBiomedParseData was created from preprocessing publicly available biomedical image segmentation datasets. Check a subset of our processed datasets on HuggingFace: \u003ca href=\"https://huggingface.co/datasets/microsoft/BiomedParseData\" rel=\"nofollow\"\u003ehttps://huggingface.co/datasets/microsoft/BiomedParseData\u003c/a\u003e. For the source datasets, please check the details here: \u003ca href=\"/microsoft/BiomedParse/blob/main/assets/readmes/DATASET.md\"\u003eBiomedParseData\u003c/a\u003e. As a quick start, we've samples a tiny demo dataset at biomedparse_datasets/BiomedParseData-Demo\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Checkpoints\u003c/h2\u003e\u003ca id=\"user-content-model-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Model Checkpoints\" href=\"#model-checkpoints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe host our model checkpoints on HuggingFace here: \u003ca href=\"https://huggingface.co/microsoft/BiomedParse\" rel=\"nofollow\"\u003ehttps://huggingface.co/microsoft/BiomedParse\u003c/a\u003e. See example code below on model loading.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlease expect future updates of the model as we are making it more robust and powerful based on feedbacks from the community. We recomment using the latest version of the model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning Inference with BiomedParse\u003c/h2\u003e\u003ca id=\"user-content-running-inference-with-biomedparse\" class=\"anchor\" aria-label=\"Permalink: Running Inference with BiomedParse\" href=\"#running-inference-with-biomedparse\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe’ve streamlined the process for running inference using BiomedParse. Below are details and resources to help you get started.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to Run Inference\u003c/h3\u003e\u003ca id=\"user-content-how-to-run-inference\" class=\"anchor\" aria-label=\"Permalink: How to Run Inference\" href=\"#how-to-run-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo perform inference with BiomedParse, use the provided example code and resources:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eInference Code\u003c/strong\u003e: Use the example inference script in \u003ccode\u003eexample_prediction.py\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSample Images\u003c/strong\u003e: Load and test with the provided example images located in the \u003ccode\u003eexamples\u003c/code\u003e directory.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eModel Configuration\u003c/strong\u003e: The model settings are defined in \u003ccode\u003econfigs/biomedparse_inference.yaml\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample Notebooks\u003c/h3\u003e\u003ca id=\"user-content-example-notebooks\" class=\"anchor\" aria-label=\"Permalink: Example Notebooks\" href=\"#example-notebooks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe’ve included sample notebooks to guide you through running inference with BiomedParse:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRGB Inference Example\u003c/strong\u003e: Check out the \u003ccode\u003einference_examples_RGB.ipynb\u003c/code\u003e notebook for example using normal RGB images, including Pathology, X-ray, Ultrasound, Endoscopy, Dermoscopy, OCT, Fundus.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDICOM Inference Example\u003c/strong\u003e: Check out the \u003ccode\u003einference_examples_DICOM.ipynb\u003c/code\u003e notebook for example using DICOM images.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNIFTI Inference Example\u003c/strong\u003e: Check out the \u003ccode\u003einference_examples_NIFTI.ipynb\u003c/code\u003e notebook for example using NIFTI image slices.\u003c/li\u003e\n\u003cli\u003eYou can also try a quick online demo: \u003ca href=\"https://colab.research.google.com/github/microsoft/BiomedParse/blob/main/inference_colab_demo.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Setup\u003c/h3\u003e\u003ca id=\"user-content-model-setup\" class=\"anchor\" aria-label=\"Permalink: Model Setup\" href=\"#model-setup\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from PIL import Image\nimport torch\nfrom modeling.BaseModel import BaseModel\nfrom modeling import build_model\nfrom utilities.distributed import init_distributed\nfrom utilities.arguments import load_opt_from_config_files\nfrom utilities.constants import BIOMED_CLASSES\nfrom inference_utils.inference import interactive_infer_image\nfrom inference_utils.output_processing import check_mask_stats\nimport numpy as np\n\n# Build model config\nopt = load_opt_from_config_files([\u0026quot;configs/biomedparse_inference.yaml\u0026quot;])\nopt = init_distributed(opt)\n\n# Load model from pretrained weights\n#pretrained_pth = 'pretrained/biomed_parse.pt'\npretrained_pth = 'hf_hub:microsoft/BiomedParse'\n\nmodel = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda()\nwith torch.no_grad():\n model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(BIOMED_CLASSES + [\u0026quot;background\u0026quot;], is_eval=True)\"\u003e\u003cpre\u003efrom PIL import Image\nimport torch\nfrom modeling.BaseModel import BaseModel\nfrom modeling import build_model\nfrom utilities.distributed import init_distributed\nfrom utilities.arguments import load_opt_from_config_files\nfrom utilities.constants import BIOMED_CLASSES\nfrom inference_utils.inference import interactive_infer_image\nfrom inference_utils.output_processing import check_mask_stats\nimport numpy as np\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Build model config\u003c/span\u003e\nopt = load_opt_from_config_files([\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003econfigs/biomedparse_inference.yaml\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e])\nopt = init_distributed(opt)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Load model from pretrained weights\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003epretrained_pth = 'pretrained/biomed_parse.pt'\u003c/span\u003e\npretrained_pth = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003ehf_hub:microsoft/BiomedParse\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\n\nmodel = BaseModel(opt, \u003cspan class=\"pl-en\"\u003ebuild_model(opt)).from_pretrained(pretrained_pth).eval().cuda\u003c/span\u003e()\nwith \u003cspan class=\"pl-en\"\u003etorch.no_grad\u003c/span\u003e():\n model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(BIOMED_CLASSES + [\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebackground\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e], is_eval=True)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSegmentation On Example Images\u003c/h3\u003e\u003ca id=\"user-content-segmentation-on-example-images\" class=\"anchor\" aria-label=\"Permalink: Segmentation On Example Images\" href=\"#segmentation-on-example-images\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# RGB image input of shape (H, W, 3). Currently only batch size 1 is supported.\nimage = Image.open('examples/Part_1_516_pathology_breast.png', formats=['png'])\nimage = image.convert('RGB')\n# text prompts querying objects in the image. Multiple ones can be provided.\nprompts = ['neoplastic cells', 'inflammatory cells']\n\n# load ground truth mask\ngt_masks = []\nfor prompt in prompts:\n gt_mask = Image.open(f\u0026quot;examples/Part_1_516_pathology_breast_{prompt.replace(' ', '+')}.png\u0026quot;, formats=['png'])\n gt_mask = 1*(np.array(gt_mask.convert('RGB'))[:,:,0] \u0026gt; 0)\n gt_masks.append(gt_mask)\n\npred_mask = interactive_infer_image(model, image, prompts)\n\n# prediction with ground truth mask\nfor i, pred in enumerate(pred_mask):\n gt = gt_masks[i]\n dice = (1*(pred\u0026gt;0.5) \u0026amp; gt).sum() * 2.0 / (1*(pred\u0026gt;0.5).sum() + gt.sum())\n print(f'Dice score for {prompts[i]}: {dice:.4f}')\n check_mask_stats(image, pred_mask[i]*255, 'X-Ray-Chest', text_prompt[i])\n print(f'p-value for {prompts[i]}: {p_value:.4f}')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e RGB image input of shape (H, W, 3). Currently only batch size 1 is supported.\u003c/span\u003e\nimage = Image.open(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eexamples/Part_1_516_pathology_breast.png\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e, formats=[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003epng\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e])\nimage = image.convert(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eRGB\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e text prompts querying objects in the image. Multiple ones can be provided.\u003c/span\u003e\nprompts = [\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eneoplastic cells\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003einflammatory cells\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e load ground truth mask\u003c/span\u003e\ngt_masks = []\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eprompt\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e prompts:\n gt_mask = Image.open(f\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eexamples/Part_1_516_pathology_breast_{prompt.replace(' ', '+')}.png\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, formats=[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003epng\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e])\n gt_mask = 1\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e(np.array(gt_mask.convert(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eRGB\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e))[:,:,0] \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e 0)\n gt_masks.append(gt_mask)\n\npred_mask = interactive_infer_image(model, image, prompts)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e prediction with ground truth mask\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003ei, pred\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e enumerate(pred_mask):\n gt = gt_masks[i]\n dice = (1\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e(pred\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e0.5) \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003egt).sum\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e 2.0 / (1\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e(pred\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e0.5).sum() + \u003cspan class=\"pl-en\"\u003egt.sum\u003c/span\u003e())\n print(f\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eDice score for {prompts[i]}: {dice:.4f}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e)\n check_mask_stats(image, pred_mask[i]\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e255, \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eX-Ray-Chest\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e, text_prompt[i])\n print(f\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003ep-value for {prompts[i]}: {p_value:.4f}\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDetection and recognition inference code are provided in \u003ccode\u003einference_utils/output_processing.py\u003c/code\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003echeck_mask_stats()\u003c/code\u003e: Outputs p-value for model-predicted mask for detection. Check the \u003ccode\u003einference_examples_RGB.ipynb\u003c/code\u003e notebook.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ecombine_masks()\u003c/code\u003e: Combines predictions for non-overlapping masks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetune on Your Own Data\u003c/h2\u003e\u003ca id=\"user-content-finetune-on-your-own-data\" class=\"anchor\" aria-label=\"Permalink: Finetune on Your Own Data\" href=\"#finetune-on-your-own-data\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhile BiomedParse can take in arbitrary image and text prompt, it can only reasonably segment the targets that it has learned during pretraining! If you have a specific segmentation task that the latest checkpint doesn't do well, here is the instruction on how to finetune it on your own data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRaw Image and Annotation\u003c/h3\u003e\u003ca id=\"user-content-raw-image-and-annotation\" class=\"anchor\" aria-label=\"Permalink: Raw Image and Annotation\" href=\"#raw-image-and-annotation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBiomedParse expects images and ground truth masks in 1024x1024 PNG format. For each dataset, put the raw image and mask files in the following format\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"├── biomedparse_datasets\n ├── YOUR_DATASET_NAME\n ├── train\n ├── train_mask\n ├── test\n └── test_mask\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e├── biomedparse_datasets\n ├── YOUR_DATASET_NAME\n ├── train\n ├── train_mask\n ├── test\n └── test_mask\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEach folder should contain .png files. The mask files should be binary images where pixels != 0 indicates the foreground region.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFile Name Convention\u003c/h3\u003e\u003ca id=\"user-content-file-name-convention\" class=\"anchor\" aria-label=\"Permalink: File Name Convention\" href=\"#file-name-convention\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEach file name follows certain convention as\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[IMAGE-NAME]_[MODALITY]_[SITE].png\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[IMAGE-NAME] is any string that is unique for one image. The format can be anything.\n[MODALITY] is a string for the modality, such as \"X-Ray\"\n[SITE] is the anatomic site for the image, such as \"chest\"\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOne image can be associated with multiple masks corresponding to multiple targets in the image. The mask file name convention is\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[IMAGE-NAME]_[MODALITY]_[SITE]_[TARGET].png\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[IMAGE-NAME], [MODALITY], and [SITE] are the same with the image file name.\n[TARGET] is the name of the target with spaces replaced by '+'. E.g. \"tube\" or \"chest+tube\". Make sure \"_\" doesn't appear in [TARGET].\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet Final Data File with Text Prompts\u003c/h3\u003e\u003ca id=\"user-content-get-final-data-file-with-text-prompts\" class=\"anchor\" aria-label=\"Permalink: Get Final Data File with Text Prompts\" href=\"#get-final-data-file-with-text-prompts\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn biomedparse_datasets/create-customer-datasets.py, specify YOUR_DATASET_NAME. Run the script with\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd biomedparse_datasets\npython create-customer-datasets.py\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd biomedparse_datasets\npython create-customer-datasets.py\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAfter that, the dataset folder should be of the following format\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"├── dataset_name\n ├── train\n ├── train_mask\n ├── train.json\n ├── test\n ├── test_mask\n └── test.json\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e├── dataset_name\n ├── train\n ├── train_mask\n ├── train.json\n ├── test\n ├── test_mask\n └── test.json\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRegister Your Dataset for Training and Evaluation\u003c/h3\u003e\u003ca id=\"user-content-register-your-dataset-for-training-and-evaluation\" class=\"anchor\" aria-label=\"Permalink: Register Your Dataset for Training and Evaluation\" href=\"#register-your-dataset-for-training-and-evaluation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn datasets/registration/register_biomed_datasets.py, simply add YOUR_DATASET_NAME to the datasets list. Registered datasets are ready to be added to the training and evaluation config file configs/biomed_seg_lang_v1.yaml. Your training dataset is registered as biomed_YOUR_DATASET_NAME_train, and your test dataset is biomed_YOUR_DATASET_NAME_test.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain BiomedParse\u003c/h2\u003e\u003ca id=\"user-content-train-biomedparse\" class=\"anchor\" aria-label=\"Permalink: Train BiomedParse\" href=\"#train-biomedparse\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo train the BiomedParse model, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"bash assets/scripts/train.sh\"\u003e\u003cpre\u003ebash assets/scripts/train.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will continue train the model using the training datasets you specified in configs/biomed_seg_lang_v1.yaml\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluate BiomedParse\u003c/h2\u003e\u003ca id=\"user-content-evaluate-biomedparse\" class=\"anchor\" aria-label=\"Permalink: Evaluate BiomedParse\" href=\"#evaluate-biomedparse\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo evaluate the model, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"bash assets/scripts/eval.sh\"\u003e\u003cpre\u003ebash assets/scripts/eval.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will continue evaluate the model on the test datasets you specified in configs/biomed_seg_lang_v1.yaml. We put BiomedParseData-Demo as the default. You can add any other datasets in the list.\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease cite our paper if you use the code, model, or data.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@article{zhao2024biomedparse,\n title = {A foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities},\n author = {Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng},\n journal = {Nature Methods},\n year = {2024},\n publisher = {Nature Publishing Group UK London},\n url = {https://www.nature.com/articles/s41592-024-02499-w},\n doi = {10.1038/s41592-024-02499-w}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ezhao2024biomedparse\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eA foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eZhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ejournal\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eNature Methods\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epublisher\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eNature Publishing Group UK London\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eurl\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ehttps://www.nature.com/articles/s41592-024-02499-w\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003edoi\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e10.1038/s41592-024-02499-w\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage and License Notices\u003c/h2\u003e\u003ca id=\"user-content-usage-and-license-notices\" class=\"anchor\" aria-label=\"Permalink: Usage and License Notices\" href=\"#usage-and-license-notices\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe model described in this repository is provided for research and development use only. The model is not intended for use in clinical decision-making or for any other clinical use, and the performance of the model for clinical use has not been established. You bear sole responsibility for any use of this model, including incorporation into any product intended for clinical use.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"BiomedParse","anchor":"biomedparse","htmlText":"BiomedParse"},{"level":2,"text":"News","anchor":"news","htmlText":"News"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Conda Environment Setup","anchor":"conda-environment-setup","htmlText":"Conda Environment Setup"},{"level":4,"text":"Option 1: Directly build the conda environment","anchor":"option-1-directly-build-the-conda-environment","htmlText":"Option 1: Directly build the conda environment"},{"level":4,"text":"Option 2: Create a new conda environment from scratch","anchor":"option-2-create-a-new-conda-environment-from-scratch","htmlText":"Option 2: Create a new conda environment from scratch"},{"level":2,"text":"Dataset","anchor":"dataset","htmlText":"Dataset"},{"level":2,"text":"Model Checkpoints","anchor":"model-checkpoints","htmlText":"Model Checkpoints"},{"level":2,"text":"Running Inference with BiomedParse","anchor":"running-inference-with-biomedparse","htmlText":"Running Inference with BiomedParse"},{"level":3,"text":"How to Run Inference","anchor":"how-to-run-inference","htmlText":"How to Run Inference"},{"level":3,"text":"Example Notebooks","anchor":"example-notebooks","htmlText":"Example Notebooks"},{"level":3,"text":"Model Setup","anchor":"model-setup","htmlText":"Model Setup"},{"level":3,"text":"Segmentation On Example Images","anchor":"segmentation-on-example-images","htmlText":"Segmentation On Example Images"},{"level":2,"text":"Finetune on Your Own Data","anchor":"finetune-on-your-own-data","htmlText":"Finetune on Your Own Data"},{"level":3,"text":"Raw Image and Annotation","anchor":"raw-image-and-annotation","htmlText":"Raw Image and Annotation"},{"level":3,"text":"File Name Convention","anchor":"file-name-convention","htmlText":"File Name Convention"},{"level":3,"text":"Get Final Data File with Text Prompts","anchor":"get-final-data-file-with-text-prompts","htmlText":"Get Final Data File with Text Prompts"},{"level":3,"text":"Register Your Dataset for Training and Evaluation","anchor":"register-your-dataset-for-training-and-evaluation","htmlText":"Register Your Dataset for Training and Evaluation"},{"level":2,"text":"Train BiomedParse","anchor":"train-biomedparse","htmlText":"Train BiomedParse"},{"level":2,"text":"Evaluate BiomedParse","anchor":"evaluate-biomedparse","htmlText":"Evaluate BiomedParse"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Usage and License Notices","anchor":"usage-and-license-notices","htmlText":"Usage and License Notices"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fmicrosoft%2FBiomedParse"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":".github","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of 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0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><strong>BiomedParse</strong></h1><a id="user-content-biomedparse" class="anchor" aria-label="Permalink: BiomedParse" href="#biomedparse"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository hosts the code and resources for the paper <strong>"A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities"</strong> (published in <a href="https://aka.ms/biomedparse-paper" rel="nofollow"><em>Nature Methods</em></a>).</p> <p dir="auto">[<a href="https://aka.ms/biomedparse-paper" rel="nofollow"><code>Paper</code></a>] [<a href="https://microsoft.github.io/BiomedParse/" rel="nofollow"><code>Demo</code></a>] [<a href="https://huggingface.co/microsoft/BiomedParse" rel="nofollow"><code>Model</code></a>] [<a href="https://huggingface.co/datasets/microsoft/BiomedParseData" rel="nofollow"><code>Data</code></a>] [<a href="#Citation"><code>BibTeX</code></a>]</p> <p dir="auto"><strong>BiomedParse</strong> is designed for comprehensive biomedical image analysis. It offers a unified approach to perform <strong>segmentation</strong>, <strong>detection</strong>, and <strong>recognition</strong> across diverse biomedical imaging modalities. By consolidating these tasks, BiomedParse provides an efficient and flexible tool tailored for researchers and practitioners, facilitating the interpretation and analysis of complex biomedical data.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/microsoft/BiomedParse/blob/main/assets/readmes/biomedparse_prediction_examples.png"><img src="/microsoft/BiomedParse/raw/main/assets/readmes/biomedparse_prediction_examples.png" alt="Example Predictions" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">News</h2><a id="user-content-news" class="anchor" aria-label="Permalink: News" href="#news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Jan. 9, 2025: Refined all object recognition script and added notebook with examples.</li> <li>Dec. 12, 2024: Uploaded extra datasets for finetuning on [<a href="https://huggingface.co/datasets/microsoft/BiomedParseData" rel="nofollow"><code>Data</code></a>]. Added random rotation feature for training.</li> <li>Dec. 5, 2024: The loading process of target_dist.json is optimized by automatic downloading from HuggingFace.</li> <li>Dec. 3, 2024: We added inference notebook examples in inference_example_RGB.ipynb and inference_example_NIFTI.ipynb</li> <li>Nov. 22, 2024: We added negative prediction p-value example in inference_example_DICOM.ipynb</li> <li>Nov. 18, 2024: BiomedParse is officially online in <a href="https://aka.ms/biomedparse-paper" rel="nofollow"><em>Nature Methods</em></a>!</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/microsoft/BiomedParse.git"><pre>git clone https://github.com/microsoft/BiomedParse.git</pre></div> <p dir="auto">[Notice] If inference_utils/target_dist.json is not cloned correctly, it will be automatically loaded from HuggingFace when needed.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Conda Environment Setup</h3><a id="user-content-conda-environment-setup" class="anchor" aria-label="Permalink: Conda Environment Setup" href="#conda-environment-setup"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Option 1: Directly build the conda environment</h4><a id="user-content-option-1-directly-build-the-conda-environment" class="anchor" aria-label="Permalink: Option 1: Directly build the conda environment" href="#option-1-directly-build-the-conda-environment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Under the project directory, run</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda env create -f environment.yml"><pre>conda env create -f environment.yml</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Option 2: Create a new conda environment from scratch</h4><a id="user-content-option-2-create-a-new-conda-environment-from-scratch" class="anchor" aria-label="Permalink: Option 2: Create a new conda environment from scratch" href="#option-2-create-a-new-conda-environment-from-scratch"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create -n biomedparse python=3.9.19 conda activate biomedparse"><pre>conda create -n biomedparse python=3.9.19 conda activate biomedparse</pre></div> <p dir="auto">Install Pytorch</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia"><pre>conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia</pre></div> <p dir="auto">In case there is issue with detectron2 installation, make sure your pytorch version is compatible with CUDA version on your machine at <a href="https://pytorch.org/" rel="nofollow">https://pytorch.org/</a>.</p> <p dir="auto">Install dependencies</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -r assets/requirements/requirements.txt"><pre>pip install -r assets/requirements/requirements.txt</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dataset</h2><a id="user-content-dataset" class="anchor" aria-label="Permalink: Dataset" href="#dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">BiomedParseData was created from preprocessing publicly available biomedical image segmentation datasets. Check a subset of our processed datasets on HuggingFace: <a href="https://huggingface.co/datasets/microsoft/BiomedParseData" rel="nofollow">https://huggingface.co/datasets/microsoft/BiomedParseData</a>. For the source datasets, please check the details here: <a href="/microsoft/BiomedParse/blob/main/assets/readmes/DATASET.md">BiomedParseData</a>. As a quick start, we've samples a tiny demo dataset at biomedparse_datasets/BiomedParseData-Demo</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Checkpoints</h2><a id="user-content-model-checkpoints" class="anchor" aria-label="Permalink: Model Checkpoints" href="#model-checkpoints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We host our model checkpoints on HuggingFace here: <a href="https://huggingface.co/microsoft/BiomedParse" rel="nofollow">https://huggingface.co/microsoft/BiomedParse</a>. See example code below on model loading.</p> <p dir="auto">Please expect future updates of the model as we are making it more robust and powerful based on feedbacks from the community. We recomment using the latest version of the model.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Running Inference with BiomedParse</h2><a id="user-content-running-inference-with-biomedparse" class="anchor" aria-label="Permalink: Running Inference with BiomedParse" href="#running-inference-with-biomedparse"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We’ve streamlined the process for running inference using BiomedParse. Below are details and resources to help you get started.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to Run Inference</h3><a id="user-content-how-to-run-inference" class="anchor" aria-label="Permalink: How to Run Inference" href="#how-to-run-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To perform inference with BiomedParse, use the provided example code and resources:</p> <ul dir="auto"> <li><strong>Inference Code</strong>: Use the example inference script in <code>example_prediction.py</code>.</li> <li><strong>Sample Images</strong>: Load and test with the provided example images located in the <code>examples</code> directory.</li> <li><strong>Model Configuration</strong>: The model settings are defined in <code>configs/biomedparse_inference.yaml</code>.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Example Notebooks</h3><a id="user-content-example-notebooks" class="anchor" aria-label="Permalink: Example Notebooks" href="#example-notebooks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We’ve included sample notebooks to guide you through running inference with BiomedParse:</p> <ul dir="auto"> <li><strong>RGB Inference Example</strong>: Check out the <code>inference_examples_RGB.ipynb</code> notebook for example using normal RGB images, including Pathology, X-ray, Ultrasound, Endoscopy, Dermoscopy, OCT, Fundus.</li> <li><strong>DICOM Inference Example</strong>: Check out the <code>inference_examples_DICOM.ipynb</code> notebook for example using DICOM images.</li> <li><strong>NIFTI Inference Example</strong>: Check out the <code>inference_examples_NIFTI.ipynb</code> notebook for example using NIFTI image slices.</li> <li>You can also try a quick online demo: <a href="https://colab.research.google.com/github/microsoft/BiomedParse/blob/main/inference_colab_demo.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Model Setup</h3><a id="user-content-model-setup" class="anchor" aria-label="Permalink: Model Setup" href="#model-setup"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from PIL import Image import torch from modeling.BaseModel import BaseModel from modeling import build_model from utilities.distributed import init_distributed from utilities.arguments import load_opt_from_config_files from utilities.constants import BIOMED_CLASSES from inference_utils.inference import interactive_infer_image from inference_utils.output_processing import check_mask_stats import numpy as np # Build model config opt = load_opt_from_config_files(["configs/biomedparse_inference.yaml"]) opt = init_distributed(opt) # Load model from pretrained weights #pretrained_pth = 'pretrained/biomed_parse.pt' pretrained_pth = 'hf_hub:microsoft/BiomedParse' model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda() with torch.no_grad(): model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(BIOMED_CLASSES + ["background"], is_eval=True)"><pre>from PIL import Image import torch from modeling.BaseModel import BaseModel from modeling import build_model from utilities.distributed import init_distributed from utilities.arguments import load_opt_from_config_files from utilities.constants import BIOMED_CLASSES from inference_utils.inference import interactive_infer_image from inference_utils.output_processing import check_mask_stats import numpy as np <span class="pl-c"><span class="pl-c">#</span> Build model config</span> opt = load_opt_from_config_files([<span class="pl-s"><span class="pl-pds">"</span>configs/biomedparse_inference.yaml<span class="pl-pds">"</span></span>]) opt = init_distributed(opt) <span class="pl-c"><span class="pl-c">#</span> Load model from pretrained weights</span> <span class="pl-c"><span class="pl-c">#</span>pretrained_pth = 'pretrained/biomed_parse.pt'</span> pretrained_pth = <span class="pl-s"><span class="pl-pds">'</span>hf_hub:microsoft/BiomedParse<span class="pl-pds">'</span></span> model = BaseModel(opt, <span class="pl-en">build_model(opt)).from_pretrained(pretrained_pth).eval().cuda</span>() with <span class="pl-en">torch.no_grad</span>(): model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(BIOMED_CLASSES + [<span class="pl-s"><span class="pl-pds">"</span>background<span class="pl-pds">"</span></span>], is_eval=True)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Segmentation On Example Images</h3><a id="user-content-segmentation-on-example-images" class="anchor" aria-label="Permalink: Segmentation On Example Images" href="#segmentation-on-example-images"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# RGB image input of shape (H, W, 3). Currently only batch size 1 is supported. image = Image.open('examples/Part_1_516_pathology_breast.png', formats=['png']) image = image.convert('RGB') # text prompts querying objects in the image. Multiple ones can be provided. prompts = ['neoplastic cells', 'inflammatory cells'] # load ground truth mask gt_masks = [] for prompt in prompts: gt_mask = Image.open(f"examples/Part_1_516_pathology_breast_{prompt.replace(' ', '+')}.png", formats=['png']) gt_mask = 1*(np.array(gt_mask.convert('RGB'))[:,:,0] > 0) gt_masks.append(gt_mask) pred_mask = interactive_infer_image(model, image, prompts) # prediction with ground truth mask for i, pred in enumerate(pred_mask): gt = gt_masks[i] dice = (1*(pred>0.5) & gt).sum() * 2.0 / (1*(pred>0.5).sum() + gt.sum()) print(f'Dice score for {prompts[i]}: {dice:.4f}') check_mask_stats(image, pred_mask[i]*255, 'X-Ray-Chest', text_prompt[i]) print(f'p-value for {prompts[i]}: {p_value:.4f}')"><pre><span class="pl-c"><span class="pl-c">#</span> RGB image input of shape (H, W, 3). Currently only batch size 1 is supported.</span> image = Image.open(<span class="pl-s"><span class="pl-pds">'</span>examples/Part_1_516_pathology_breast.png<span class="pl-pds">'</span></span>, formats=[<span class="pl-s"><span class="pl-pds">'</span>png<span class="pl-pds">'</span></span>]) image = image.convert(<span class="pl-s"><span class="pl-pds">'</span>RGB<span class="pl-pds">'</span></span>) <span class="pl-c"><span class="pl-c">#</span> text prompts querying objects in the image. Multiple ones can be provided.</span> prompts = [<span class="pl-s"><span class="pl-pds">'</span>neoplastic cells<span class="pl-pds">'</span></span>, <span class="pl-s"><span class="pl-pds">'</span>inflammatory cells<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> load ground truth mask</span> gt_masks = [] <span class="pl-k">for</span> <span class="pl-smi">prompt</span> <span class="pl-k">in</span> prompts: gt_mask = Image.open(f<span class="pl-s"><span class="pl-pds">"</span>examples/Part_1_516_pathology_breast_{prompt.replace(' ', '+')}.png<span class="pl-pds">"</span></span>, formats=[<span class="pl-s"><span class="pl-pds">'</span>png<span class="pl-pds">'</span></span>]) gt_mask = 1<span class="pl-k">*</span>(np.array(gt_mask.convert(<span class="pl-s"><span class="pl-pds">'</span>RGB<span class="pl-pds">'</span></span>))[:,:,0] <span class="pl-k">></span> 0) gt_masks.append(gt_mask) pred_mask = interactive_infer_image(model, image, prompts) <span class="pl-c"><span class="pl-c">#</span> prediction with ground truth mask</span> <span class="pl-k">for</span> <span class="pl-smi">i, pred</span> <span class="pl-k">in</span> enumerate(pred_mask): gt = gt_masks[i] dice = (1<span class="pl-k">*</span>(pred<span class="pl-k">></span>0.5) <span class="pl-k">&</span> <span class="pl-en">gt).sum</span>() <span class="pl-k">*</span> 2.0 / (1<span class="pl-k">*</span>(pred<span class="pl-k">></span>0.5).sum() + <span class="pl-en">gt.sum</span>()) print(f<span class="pl-s"><span class="pl-pds">'</span>Dice score for {prompts[i]}: {dice:.4f}<span class="pl-pds">'</span></span>) check_mask_stats(image, pred_mask[i]<span class="pl-k">*</span>255, <span class="pl-s"><span class="pl-pds">'</span>X-Ray-Chest<span class="pl-pds">'</span></span>, text_prompt[i]) print(f<span class="pl-s"><span class="pl-pds">'</span>p-value for {prompts[i]}: {p_value:.4f}<span class="pl-pds">'</span></span>)</pre></div> <p dir="auto">Detection and recognition inference code are provided in <code>inference_utils/output_processing.py</code>.</p> <ul dir="auto"> <li><code>check_mask_stats()</code>: Outputs p-value for model-predicted mask for detection. Check the <code>inference_examples_RGB.ipynb</code> notebook.</li> <li><code>combine_masks()</code>: Combines predictions for non-overlapping masks.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Finetune on Your Own Data</h2><a id="user-content-finetune-on-your-own-data" class="anchor" aria-label="Permalink: Finetune on Your Own Data" href="#finetune-on-your-own-data"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">While BiomedParse can take in arbitrary image and text prompt, it can only reasonably segment the targets that it has learned during pretraining! If you have a specific segmentation task that the latest checkpint doesn't do well, here is the instruction on how to finetune it on your own data.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Raw Image and Annotation</h3><a id="user-content-raw-image-and-annotation" class="anchor" aria-label="Permalink: Raw Image and Annotation" href="#raw-image-and-annotation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">BiomedParse expects images and ground truth masks in 1024x1024 PNG format. For each dataset, put the raw image and mask files in the following format</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="├── biomedparse_datasets ├── YOUR_DATASET_NAME ├── train ├── train_mask ├── test └── test_mask"><pre class="notranslate"><code>├── biomedparse_datasets ├── YOUR_DATASET_NAME ├── train ├── train_mask ├── test └── test_mask </code></pre></div> <p dir="auto">Each folder should contain .png files. The mask files should be binary images where pixels != 0 indicates the foreground region.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">File Name Convention</h3><a id="user-content-file-name-convention" class="anchor" aria-label="Permalink: File Name Convention" href="#file-name-convention"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Each file name follows certain convention as</p> <p dir="auto">[IMAGE-NAME]_[MODALITY]_[SITE].png</p> <p dir="auto">[IMAGE-NAME] is any string that is unique for one image. The format can be anything. [MODALITY] is a string for the modality, such as "X-Ray" [SITE] is the anatomic site for the image, such as "chest"</p> <p dir="auto">One image can be associated with multiple masks corresponding to multiple targets in the image. The mask file name convention is</p> <p dir="auto">[IMAGE-NAME]_[MODALITY]_[SITE]_[TARGET].png</p> <p dir="auto">[IMAGE-NAME], [MODALITY], and [SITE] are the same with the image file name. [TARGET] is the name of the target with spaces replaced by '+'. E.g. "tube" or "chest+tube". Make sure "_" doesn't appear in [TARGET].</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Get Final Data File with Text Prompts</h3><a id="user-content-get-final-data-file-with-text-prompts" class="anchor" aria-label="Permalink: Get Final Data File with Text Prompts" href="#get-final-data-file-with-text-prompts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In biomedparse_datasets/create-customer-datasets.py, specify YOUR_DATASET_NAME. Run the script with</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd biomedparse_datasets python create-customer-datasets.py"><pre class="notranslate"><code>cd biomedparse_datasets python create-customer-datasets.py </code></pre></div> <p dir="auto">After that, the dataset folder should be of the following format</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="├── dataset_name ├── train ├── train_mask ├── train.json ├── test ├── test_mask └── test.json"><pre class="notranslate"><code>├── dataset_name ├── train ├── train_mask ├── train.json ├── test ├── test_mask └── test.json </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Register Your Dataset for Training and Evaluation</h3><a id="user-content-register-your-dataset-for-training-and-evaluation" class="anchor" aria-label="Permalink: Register Your Dataset for Training and Evaluation" href="#register-your-dataset-for-training-and-evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In datasets/registration/register_biomed_datasets.py, simply add YOUR_DATASET_NAME to the datasets list. Registered datasets are ready to be added to the training and evaluation config file configs/biomed_seg_lang_v1.yaml. Your training dataset is registered as biomed_YOUR_DATASET_NAME_train, and your test dataset is biomed_YOUR_DATASET_NAME_test.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Train BiomedParse</h2><a id="user-content-train-biomedparse" class="anchor" aria-label="Permalink: Train BiomedParse" href="#train-biomedparse"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To train the BiomedParse model, run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="bash assets/scripts/train.sh"><pre>bash assets/scripts/train.sh</pre></div> <p dir="auto">This will continue train the model using the training datasets you specified in configs/biomed_seg_lang_v1.yaml</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Evaluate BiomedParse</h2><a id="user-content-evaluate-biomedparse" class="anchor" aria-label="Permalink: Evaluate BiomedParse" href="#evaluate-biomedparse"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To evaluate the model, run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="bash assets/scripts/eval.sh"><pre>bash assets/scripts/eval.sh</pre></div> <p dir="auto">This will continue evaluate the model on the test datasets you specified in configs/biomed_seg_lang_v1.yaml. We put BiomedParseData-Demo as the default. You can add any other datasets in the list.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please cite our paper if you use the code, model, or data.</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@article{zhao2024biomedparse, title = {A foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities}, author = {Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng}, journal = {Nature Methods}, year = {2024}, publisher = {Nature Publishing Group UK London}, url = {https://www.nature.com/articles/s41592-024-02499-w}, doi = {10.1038/s41592-024-02499-w} }"><pre><span class="pl-k">@article</span>{<span class="pl-en">zhao2024biomedparse</span>, <span class="pl-s">title</span> = <span class="pl-s"><span class="pl-pds">{</span>A foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities<span class="pl-pds">}</span></span>, <span class="pl-s">author</span> = <span class="pl-s"><span class="pl-pds">{</span>Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span> = <span class="pl-s"><span class="pl-pds">{</span>Nature Methods<span class="pl-pds">}</span></span>, <span class="pl-s">year</span> = <span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span>, <span class="pl-s">publisher</span> = <span class="pl-s"><span class="pl-pds">{</span>Nature Publishing Group UK London<span class="pl-pds">}</span></span>, <span class="pl-s">url</span> = <span class="pl-s"><span class="pl-pds">{</span>https://www.nature.com/articles/s41592-024-02499-w<span class="pl-pds">}</span></span>, <span class="pl-s">doi</span> = <span class="pl-s"><span class="pl-pds">{</span>10.1038/s41592-024-02499-w<span class="pl-pds">}</span></span> }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage and License Notices</h2><a id="user-content-usage-and-license-notices" class="anchor" aria-label="Permalink: Usage and License Notices" href="#usage-and-license-notices"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The model described in this repository is provided for research and development use only. The model is not intended for use in clinical decision-making or for any other clinical use, and the performance of the model for clinical use has not been established. You bear sole responsibility for any use of this model, including incorporation into any product intended for clinical use.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="cgorjLisXUc5axZoXffYwt+trLxsHT6PSolPyV03vC4r1Cmrt+W76Qh1tt16NdVJA60CJg0K1C4Hps38DScDww==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> BiomedParse: A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path> </svg> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://microsoft.github.io/BiomedParse/" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://microsoft.github.io/BiomedParse/">microsoft.github.io/BiomedParse/</a> </span> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" 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1-.895 0l-.221-.163c-1.036-.767-2.753-2.037-4.249-3.563C1.51 10.008.007 7.952.002 5.762a4.614 4.614 0 0 1 1.353-3.407C3.123.585 6.223.537 8.048 2.24Zm-1.153.983c-1.25-1.033-3.321-.967-4.48.191a3.115 3.115 0 0 0-.913 2.335c0 1.556 1.109 3.24 2.652 4.813C5.463 11.898 6.96 13.032 8 13.805c.353-.262.758-.565 1.191-.905l-1.326-1.223a.75.75 0 0 1 1.018-1.102l1.48 1.366c.328-.281.659-.577.984-.887L9.99 9.802a.75.75 0 1 1 1.019-1.103l1.384 1.28c.295-.329.566-.661.81-.995L12.92 8.7l-1.167-1.168c-.674-.671-1.78-.664-2.474.03-.268.269-.538.537-.802.797-.893.882-2.319.843-3.185-.032-.346-.35-.693-.697-1.043-1.047a.75.75 0 0 1-.04-1.016c.162-.191.336-.401.52-.623.62-.748 1.356-1.637 2.166-2.417Zm7.112 4.442c.313-.65.491-1.293.491-1.916v-.001c0-.614-.088-1.045-.23-1.385-.143-.339-.357-.633-.673-.949a3.111 3.111 0 0 0-2.218-.915c-1.092.003-2.165.627-3.226 1.602-.823.755-1.554 1.637-2.228 2.45l-.127.154.562.566a.755.755 0 0 0 1.066.02l.794-.79c1.258-1.258 3.312-1.31 4.594-.032.396.394.792.791 1.173 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0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> Security policy </a> </div> <include-fragment src="/microsoft/BiomedParse/hovercards/citation/sidebar_partial?tree_name=main"> </include-fragment> <div class="mt-2"> <a href="/microsoft/BiomedParse/activity" data-view-component="true" class="Link Link--muted"><svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon 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class="color-fg-muted">Custom properties</span></a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/microsoft/BiomedParse/stargazers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>413</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/microsoft/BiomedParse/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>8</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/microsoft/BiomedParse/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>43</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2FBiomedParse&report=microsoft+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/microsoft/BiomedParse/used_by_list" accept="text/fragment+html"> 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width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/eltociear" class="" data-hovercard-type="user" data-hovercard-url="/users/eltociear/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/22633385?s=64&v=4" alt="@eltociear" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#DA5B0B !important;;width: 79.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 19.6%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 1.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 0.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#ecdebe !important;;width: 0.0%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/microsoft/BiomedParse/search?l=jupyter-notebook" data-ga-click="Repository, language stats search click, location:repo 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no-underline text-small mr-3" href="/microsoft/BiomedParse/search?l=cuda" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3A4E3A;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cuda</span> <span>1.1%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/microsoft/BiomedParse/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>0.1%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/microsoft/BiomedParse/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.1%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/microsoft/BiomedParse/search?l=roff" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#ecdebe;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Roff</span> <span>0.0%</span> </a> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12.5.75C6.146.75 1 5.896 1 12.25c0 5.089 3.292 9.387 7.863 10.91.575.101.79-.244.79-.546 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