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GitHub - MIC-DKFZ/RegRCNN: This repository holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels. It is a fork of MIC-DKFZ/medicaldetectiontoolkit with regression capabilites.
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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2"> Search syntax tips </a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" 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3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="NTirjuy7RLp3rC4ymaZpAvgrycU6/KxFFl8z9yaBgAl8RI0SOdKb1jk9+pvejNzA/uwN0FeGrnyBvKD9OmX+SA==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> 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rel=\"nofollow\"\u003eDivision of Medical Image Computing (MIC)\u003c/a\u003e.\nPlease make sure that your usage of this code is in compliance with the code \u003ca href=\"/MIC-DKFZ/RegRCNN/blob/master/LICENSE\"\u003elicense\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRelease Notes\u003c/h2\u003e\u003ca id=\"user-content-release-notes\" class=\"anchor\" aria-label=\"Permalink: Release Notes\" href=\"#release-notes\"\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\"\u003e0.0.2 - Now with updated torch (\u0026gt;=1.3) and torchvision (\u0026gt;=0.4) dependencies and on python-3.7.5 basis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\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 holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels [1].\nThe framework is a fork of MIC's \u003ca href=\"https://github.com/MIC-DKFZ/medicaldetectiontoolkit\"\u003emedicaldetectiontoolkit\u003c/a\u003e with added regression\ncapabilities.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs below figure shows, the regression capability allows for the preservation of ordinal relations in the training signal as opposed to a standard categorical classification loss like the cross entropy loss (see publication for details).\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/teaser.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/teaser.png\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cbr\u003e\nNetwork Reg R-CNN is a version of Mask R-CNN [2] but with a regressor in place of the object-class head (see figure below). In this scenario, the first stage makes foreground (fg) vs background (bg) detections, then the regression head determines the class on an ordinal scale. Consequently, prediction confidence scores are taken from the first stage as opposed to the head in the original Mask R-CNN.\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/regrcnn.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/regrcnn.png\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eIn the configs file of a data set in the framework, you may set attribute self.prediction_tasks = [\"task\"] to a value \"task\" from [\"class\", \"regression_bin\", \"regression\"]. \"class\" produces the same behavior as the original framework, i.e., standard object-detection behavior. \"regression\" on the other hand, swaps the class head of network Mask R-CNN [2] for a regression head. Consequently, objects are identified as fg/bg and then the class is decided by the regressor. For the sake of comparability, \"regression_bin\" produces a similar behavior but with a classification head. Both methods should be evaluated with the (implemented) Average Viewpoint Precision instead of only Average Precision.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBelow you will found a description of the general framework operations and handling. Basic framework functionality and description are for the most part identical to the original \u003ca href=\"https://github.com/MIC-DKFZ/medicaldetectiontoolkit\"\u003emedicaldetectiontoolkit\u003c/a\u003e.\u003c/p\u003e\n\u003cbr\u003e\n[1] Ramien, Gregor et al., \u003ca href=\"https://arxiv.org/abs/1907.12915\" rel=\"nofollow\"\u003e\"Reg R-CNN: Lesion Detection and Grading under Noisy Labels\"\u003c/a\u003e. In: UNSURE Workshop at MICCAI, 2019.\u003cbr\u003e\n[2] He, Kaiming, et al. \u003ca href=\"https://arxiv.org/abs/1703.06870\" rel=\"nofollow\"\u003e\"Mask R-CNN\"\u003c/a\u003e ICCV, 2017\u003cbr\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\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 is a comprehensive framework for object detection featuring:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2D + 3D implementations of common object detectors: e.g., Mask R-CNN [2], Retina Net [3], Retina U-Net [4].\u003c/li\u003e\n\u003cli\u003eModular and light-weight structure ensuring sharing of all processing steps (incl. backbone architecture) for comparability of models.\u003c/li\u003e\n\u003cli\u003etraining with bounding box and/or pixel-wise annotations.\u003c/li\u003e\n\u003cli\u003edynamic patching and tiling of 2D + 3D images (for training and inference).\u003c/li\u003e\n\u003cli\u003eweighted consolidation of box predictions across patch-overlaps, ensembles, and dimensions [4] or standard non-maximum suppression.\u003c/li\u003e\n\u003cli\u003emonitoring + evaluation simultaneously on object and patient level.\u003c/li\u003e\n\u003cli\u003e2D + 3D output visualizations.\u003c/li\u003e\n\u003cli\u003eintegration of COCO mean average precision metric [5].\u003c/li\u003e\n\u003cli\u003eintegration of MIC-DKFZ batch generators for extensive data augmentation [6].\u003c/li\u003e\n\u003cli\u003epossible evaluation of instance segmentation and/or semantic segmentation by dice scores.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003e[3] Lin, Tsung-Yi, et al. \u003ca href=\"https://arxiv.org/abs/1708.02002\" rel=\"nofollow\"\u003e\"Focal Loss for Dense Object Detection\"\u003c/a\u003e TPAMI, 2018.\u003cbr\u003e\n[4] Jaeger, Paul et al. \u003ca href=\"http://arxiv.org/abs/1811.08661\" rel=\"nofollow\"\u003e \"Retina U-Net: Embarrassingly Simple Exploitation\nof Segmentation Supervision for Medical Object Detection\" \u003c/a\u003e, 2018\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[5] \u003ca href=\"https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py\"\u003ehttps://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py\u003c/a\u003e\u003cbr\u003e\n[6] \u003ca href=\"https://github.com/MIC-DKFZ/batchgenerators\"\u003ehttps://github.com/MIC-DKFZ/batchgenerators\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to cite this code\u003c/h2\u003e\u003ca id=\"user-content-how-to-cite-this-code\" class=\"anchor\" aria-label=\"Permalink: How to cite this code\" href=\"#how-to-cite-this-code\"\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 the Reg R-CNN publication [1] or the original publication [4] depending on what features you use.\u003c/p\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\u003cp dir=\"auto\"\u003eSetup package in virtual environment\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/MIC-DKFZ/RegRCNN.git.\ncd RegRCNN\nvirtualenv -p python3.7 regrcnn_env\nsource regrcnn_env/bin/activate\npython setup.py install\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit clone https://github.com/MIC-DKFZ/RegRCNN.git.\ncd RegRCNN\nvirtualenv -p python3.7 regrcnn_env\nsource regrcnn_env/bin/activate\npython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustom Extensions\u003c/h5\u003e\u003ca id=\"user-content-custom-extensions\" class=\"anchor\" aria-label=\"Permalink: Custom Extensions\" href=\"#custom-extensions\"\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 framework uses two custom mixed C++/CUDA extensions: Non-maximum suppression (NMS) and RoIAlign. Both are adapted from the original pytorch extensions (under torchvision.ops.boxes and ops.roialign).\nThe extensions are automatically compiled from the provided source files under RegRCNN/custom_extensions with above setup.py.\nHowever, the extensions need to be compiled specifically for certain GPU architectures. Hence, please ensure that the architectures you need are included in your shell's\nenvironment variable \u003ccode\u003eTORCH_CUDA_ARCH_LIST\u003c/code\u003e before compilation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExample: You want to use the modules with the new TITAN RTX GPU, which has\nCompute Capability 7.5 (Turing Architecture), but sometimes you also want to use it with a TITAN Xp (6.1, Pascal). Before installation you need to\n\u003ccode\u003eexport TORCH_CUDA_ARCH_LIST=\"6.1;7.5\"\u003c/code\u003e. A link list of GPU model names to Compute Capability can be found here: \u003ca href=\"https://developer.nvidia.com/cuda-gpus\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/cuda-gpus\u003c/a\u003e.\nNote: If you'd like to import the raw extensions (not the wrapper modules), be sure to import torch first.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrepare the Data\u003c/h2\u003e\u003ca id=\"user-content-prepare-the-data\" class=\"anchor\" aria-label=\"Permalink: Prepare the Data\" href=\"#prepare-the-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\"\u003eThis framework is meant for you to be able to train models on your own data sets.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn order to include a data set in the framework, create a new folder in RegRCNN/datasets, for instance \"example_data\". Your data set needs to have a config file in the style of the provided example data sets \"lidc\" and \"toy\". It also needs a data loader meeting the same requirements as the provided examples. Likely, you will also need a preprocessing script that transforms your data (once per data set creation, i.e., not a repetitive operation) into a suitable and easily processable format.\nImportant requirements:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe framework expects numpy arrays as data and segmentation ground truth input.\u003c/li\u003e\n\u003cli\u003eSegmentations need to be suited for object detection, i.e., Regions of Interest (RoIs) need to be marked by integers (RoI-ID) in the segmentation volume (0 is background). Corresponding properties of a RoI, e.g., the \"class_targets\" need to be provided in a separate array or list with (RoI-ID - 1) corresponding to the index of the property in the list (-1 due to zero-indexing). Example: A data volume contains two RoIs. The second RoI is marked in the segmentation by number 2. The \"class_targets\" info associated with the data volume holds the list [2, 3]. Hence, RoI-ID 2 is assigned class 3.\u003c/li\u003e\n\u003cli\u003eThis framework uses a modified version of MIC's batchgenerators' segmentation-to-bounding-box conversion tool. In this version, \"class_targets\", i.e., object classes start at 1, 0 is reserved for background. Thus, if you use \"ConvertSegToBoundingBoxCoordinates\" classes in your preprocessed data need to start at 1, not 0.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eTwo example data loaders are provided in RegRCNN/datasets. The way I load data is to have a preprocessing script, which after preprocessing saves the data of whatever data type into numpy arrays (this is just run once). During training / testing, the data loader then loads these numpy arrays dynamically. Please note the data input side is meant to be customized by you according to your own needs and the provided data loaders are merely examples: LIDC has a powerful data loader that handles 2D/3D inputs and is optimized for patch-based training and inference. Due to the large data volumes of LIDC, this loader is slow. The provided toy data set, however, is light weight and a good starting point to get familiar with the framework. It is fully creatable from scratch within a few minutes with RegRCNN/datasets/toy/generate_toys.py.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExecute\u003c/h2\u003e\u003ca id=\"user-content-execute\" class=\"anchor\" aria-label=\"Permalink: Execute\" href=\"#execute\"\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\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSet I/O paths, model and training specifics in the configs file: RegRCNN/datasets/\u003cem\u003eyour_dataset\u003c/em\u003e/configs.py\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ei) Train the model:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python exec.py --mode train --dataset_name your_dataset --exp_dir path/to/experiment/directory \"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython exec.py --mode train --dataset_name your_dataset --exp_dir path/to/experiment/directory \n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis copies snapshots of configs and model to the specified exp_dir, where all outputs will be saved. By default, the data is split into 60% training and 20% validation and 20% testing data to perform a 5-fold cross validation (can be changed to hold-out test set in configs) and all folds will be trained iteratively. In order to train a single fold, specify it using the folds arg:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python exec.py --folds 0 1 2 .... # specify any combination of folds [0-configs.n_cv_splits]\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython exec.py --folds 0 1 2 .... # specify any combination of folds [0-configs.n_cv_splits]\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eii) Alternatively, train and test consecutively:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python exec.py --mode train_test --dataset_name your_dataset --exp_dir path/to/experiment/directory \"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython exec.py --mode train_test --dataset_name your_dataset --exp_dir path/to/experiment/directory \n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRun inference:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python exec.py --mode test --exp_dir path/to/experiment/directory \"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython exec.py --mode test --exp_dir path/to/experiment/directory \n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis runs the prediction pipeline and saves all results to exp_dir.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eAdditional settings:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCheck the args parser in exec.py to see which arguments and modes are available.\u003c/li\u003e\n\u003cli\u003eE.g., you may pass \u003ccode\u003e-d\u003c/code\u003e or \u003ccode\u003e--dev\u003c/code\u003e to enable a short development run of the whole train_test procedure (small batch size, only one epoch, two folds, one test patient, etc.).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModels\u003c/h2\u003e\u003ca id=\"user-content-models\" class=\"anchor\" aria-label=\"Permalink: Models\" href=\"#models\"\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 framework features models explored in [4] (implemented in 2D + 3D): The proposed Retina U-Net, a simple but effective Architecture fusing state-of-the-art semantic segmentation with object detection,\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/retu_figure.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/retu_figure.png\" width=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cbr\u003e\nalso implementations of prevalent object detectors, such as Mask R-CNN, Faster R-CNN+ (Faster R-CNN w\\ RoIAlign), Retina Net, Detection U-Net (a U-Net like segmentation architecture with heuristics for object detection.)\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/baseline_figure.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/baseline_figure.png\" width=\"85%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining annotations\u003c/h2\u003e\u003ca id=\"user-content-training-annotations\" class=\"anchor\" aria-label=\"Permalink: Training annotations\" href=\"#training-annotations\"\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 framework features training with pixelwise and/or bounding box annotations. To overcome the issue of box coordinates in\ndata augmentation, we feed the annotation masks through data augmentation (create a pseudo mask, if only bounding box annotations provided) and draw the boxes afterwards.\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/annotations.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/annotations.png\" width=\"85%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eThe framework further handles two types of pixel-wise annotations:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eA label map with individual ROIs identified by increasing label values, accompanied by a vector containing in each position the class target for the lesion with the corresponding label (for this mode set get_rois_from_seg_flag = False when calling ConvertSegToBoundingBoxCoordinates in your Data Loader). This is usual use case as explained in section \"Prepare the data\".\u003c/li\u003e\n\u003cli\u003eA binary label map. There is only one foreground class and single lesions are not identified. All lesions have the same class target (foreground). In this case the data loader runs a Connected Component Labelling algorithm to create processable lesion - class target pairs on the fly (for this mode set get_rois_from_seg_flag = True when calling ConvertSegToBoundingBoxCoordinates in your data loader).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrediction pipeline\u003c/h2\u003e\u003ca id=\"user-content-prediction-pipeline\" class=\"anchor\" aria-label=\"Permalink: Prediction pipeline\" href=\"#prediction-pipeline\"\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 framework provides an inference module, which automatically handles patching of inputs, and tiling, ensembling, and weighted consolidation of output predictions:\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/prediction_pipeline.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/prediction_pipeline.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConsolidation of predictions\u003c/h2\u003e\u003ca id=\"user-content-consolidation-of-predictions\" class=\"anchor\" aria-label=\"Permalink: Consolidation of predictions\" href=\"#consolidation-of-predictions\"\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeighted Box Clustering\u003c/h3\u003e\u003ca id=\"user-content-weighted-box-clustering\" class=\"anchor\" aria-label=\"Permalink: Weighted Box Clustering\" href=\"#weighted-box-clustering\"\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\"\u003eMultiple predictions of the same image (from test time augmentations, tested epochs and overlapping patches), result in a high amount of boxes (or cubes), which need to be consolidated. In semantic segmentation, the final output would typically be obtained by averaging every pixel over all predictions. As described in [4], \u003cstrong\u003eweighted box clustering\u003c/strong\u003e (WBC) does this for box predictions:\u003cbr\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/wcs_text.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/wcs_text.png\" width=\"650\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/wcs_readme.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/wcs_readme.png\" width=\"800\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo enable WBC, set self.clustering = \"wbc\" in your configs file.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNon-Maximum Suppression\u003c/h3\u003e\u003ca id=\"user-content-non-maximum-suppression\" class=\"anchor\" aria-label=\"Permalink: Non-Maximum Suppression\" href=\"#non-maximum-suppression\"\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\"\u003eTest-time predictions can alternatively be aggregated with standard non-maximum suppression. In your configs file, simply set self.clustering = \"nms\" instead of \"wbc\".\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs a further alternative you may also choose no test-time aggregation by setting self.clustering = None.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVisualization / Monitoring\u003c/h2\u003e\u003ca id=\"user-content-visualization--monitoring\" class=\"anchor\" aria-label=\"Permalink: Visualization / Monitoring\" href=\"#visualization--monitoring\"\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 opposition to the original framework, this fork uses tensorboard for monitoring training and validation progress. Since, for now, the framework cannot easily be updated to pytorch \u0026gt;= 1.x, we need third-party package \u003ca href=\"https://github.com/lanpa/tensorboardX\"\u003etensorboardX\u003c/a\u003e to use tensorboard with pytorch.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can set an applicable choice of implemented metrics like \"ap\" for Average Precision or \"auc\" for patient-level ROC-AUC in the configs under self.metrics = [...]. Metrics are then evaluated by evaluator.py and recorded in monitor_metrics. logger.metrics2tboard sends monitor_metrics to your tensorboard logfiles at the end of each epoch.\nYou need to separately start a virtual tensorboard server, pass it your experiment directory (or directories, but it crashes if its more than ~5 experiments) and navigate to the server address. (You can also read up on tensoardboard usage in the original documentation).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample:\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example:\" href=\"#example\"\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\u003col dir=\"auto\"\u003e\n\u003cli\u003eActivate your virtualenv where tensorboard is installed.\u003c/li\u003e\n\u003cli\u003eStart tensorboard server. For instance, your experiment directory is\n\u003cem\u003eyourexpdir\u003c/em\u003e: \u003cbr\u003e\n\u003ccode\u003etensorboard --port 6007 --logdir yourexpdir\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eNavigate to \u003ccode\u003elocalhost:6007\u003c/code\u003e in your browser.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOutput monitoring\u003c/h3\u003e\u003ca id=\"user-content-output-monitoring\" class=\"anchor\" aria-label=\"Permalink: Output monitoring\" href=\"#output-monitoring\"\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\"\u003eFor qualitative monitoring, example plots are saved to \u003cem\u003eyourexpdir\u003c/em\u003e/plots for training and validation and \u003cem\u003eyourexpdir\u003c/em\u003e/test/example_plots for testing. Note, that test-time example plots may contain unconsolidated predictions over test-time augmentations, thereby possibly showing many overlapping and/or noisy predictions. You may adapt/use separate file RegRCNN/inference_analysis.py to create clean and nice plots of (consolidated) test-time predictions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBalancing Mechanism of Example Data Loader\u003c/h2\u003e\u003ca id=\"user-content-balancing-mechanism-of-example-data-loader\" class=\"anchor\" aria-label=\"Permalink: Balancing Mechanism of Example Data Loader\" href=\"#balancing-mechanism-of-example-data-loader\"\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 data loaders of the provided example data sets employ a custom mechanism with the goal of assembling target-balanced batches or training sequences. I.e., the amount of examples shown per target class should be near balance.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe mechanism creates a sampling-likelihood distribution, as shown below, over all available patients (PIDs). At batch generation, some patients are drawn according to this distribution, others are drawn completely randomly (according to a uniform distribution across all patients). The ratio of uniformly and target-dependently drawn patients is set in your configs file by configs.batch_random_ratio. configs.balance_target determines which targets are considered for the balancing distribution.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe balancing distribution assigns probabilities s.t. expected occurrences of fg and bg RoIs among all classes are as similar as possible.\nThe balance is naturally limited by multiple RoIs occurring in the same patient (e.g, if each patient has 4 RoIs of class 1 and 1 RoI of class 2 the best balancing ratio achievable is still 4:1).\nSee utils/dataloader_utils.BatchGenerator.balance_target_distribution.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExperience has shown, that showing at least one foreground example in each batch is most critical, other properties have less impact.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/MIC-DKFZ/RegRCNN/blob/master/assets/train_gen.png\"\u003e\u003cimg src=\"/MIC-DKFZ/RegRCNN/raw/master/assets/train_gen.png\" width=\"800\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUnittests\u003c/h2\u003e\u003ca id=\"user-content-unittests\" class=\"anchor\" aria-label=\"Permalink: Unittests\" href=\"#unittests\"\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\"\u003eunittests.py contains some verification and testing procedures, which, however, need you to adjust paths in the TestCase classes before execution. Tests can be used, for instance, to verify if your cross-validation folds have been created correctly, or if separate experiments have the same fold splits.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h1\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\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 framework is published under the \u003ca href=\"https://github.com/MIC-DKFZ/RegRCNN/blob/master/LICENSE\"\u003eAPACHE 2.0 License\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Release Notes","anchor":"release-notes","htmlText":"Release Notes"},{"level":2,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"How to cite this code","anchor":"how-to-cite-this-code","htmlText":"How to cite this code"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":5,"text":"Custom Extensions","anchor":"custom-extensions","htmlText":"Custom Extensions"},{"level":2,"text":"Prepare the Data","anchor":"prepare-the-data","htmlText":"Prepare the Data"},{"level":2,"text":"Execute","anchor":"execute","htmlText":"Execute"},{"level":2,"text":"Models","anchor":"models","htmlText":"Models"},{"level":2,"text":"Training annotations","anchor":"training-annotations","htmlText":"Training annotations"},{"level":2,"text":"Prediction pipeline","anchor":"prediction-pipeline","htmlText":"Prediction pipeline"},{"level":2,"text":"Consolidation of predictions","anchor":"consolidation-of-predictions","htmlText":"Consolidation of predictions"},{"level":3,"text":"Weighted Box Clustering","anchor":"weighted-box-clustering","htmlText":"Weighted Box Clustering"},{"level":3,"text":"Non-Maximum Suppression","anchor":"non-maximum-suppression","htmlText":"Non-Maximum Suppression"},{"level":2,"text":"Visualization / Monitoring","anchor":"visualization--monitoring","htmlText":"Visualization / Monitoring"},{"level":3,"text":"Example:","anchor":"example","htmlText":"Example:"},{"level":3,"text":"Output monitoring","anchor":"output-monitoring","htmlText":"Output monitoring"},{"level":2,"text":"Balancing Mechanism of Example Data Loader","anchor":"balancing-mechanism-of-example-data-loader","htmlText":"Balancing Mechanism of Example Data Loader"},{"level":2,"text":"Unittests","anchor":"unittests","htmlText":"Unittests"},{"level":1,"text":"License","anchor":"license","htmlText":"License"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FMIC-DKFZ%2FRegRCNN"}},{"displayName":"LICENSE","repoName":"RegRCNN","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FMIC-DKFZ%2FRegRCNN"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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Please make sure that your usage of this code is in compliance with the code <a href="/MIC-DKFZ/RegRCNN/blob/master/LICENSE">license</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Release Notes</h2><a id="user-content-release-notes" class="anchor" aria-label="Permalink: Release Notes" href="#release-notes"><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">0.0.2 - Now with updated torch (>=1.3) and torchvision (>=0.4) dependencies and on python-3.7.5 basis.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Introduction</h2><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><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 holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels [1]. The framework is a fork of MIC's <a href="https://github.com/MIC-DKFZ/medicaldetectiontoolkit">medicaldetectiontoolkit</a> with added regression capabilities.</p> <p dir="auto">As below figure shows, the regression capability allows for the preservation of ordinal relations in the training signal as opposed to a standard categorical classification loss like the cross entropy loss (see publication for details).</p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/teaser.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/teaser.png" width="50%" style="max-width: 100%;"></a></p><br> Network Reg R-CNN is a version of Mask R-CNN [2] but with a regressor in place of the object-class head (see figure below). In this scenario, the first stage makes foreground (fg) vs background (bg) detections, then the regression head determines the class on an ordinal scale. Consequently, prediction confidence scores are taken from the first stage as opposed to the head in the original Mask R-CNN. <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/regrcnn.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/regrcnn.png" width="50%" style="max-width: 100%;"></a></p><br> <p dir="auto">In the configs file of a data set in the framework, you may set attribute self.prediction_tasks = ["task"] to a value "task" from ["class", "regression_bin", "regression"]. "class" produces the same behavior as the original framework, i.e., standard object-detection behavior. "regression" on the other hand, swaps the class head of network Mask R-CNN [2] for a regression head. Consequently, objects are identified as fg/bg and then the class is decided by the regressor. For the sake of comparability, "regression_bin" produces a similar behavior but with a classification head. Both methods should be evaluated with the (implemented) Average Viewpoint Precision instead of only Average Precision.</p> <p dir="auto">Below you will found a description of the general framework operations and handling. Basic framework functionality and description are for the most part identical to the original <a href="https://github.com/MIC-DKFZ/medicaldetectiontoolkit">medicaldetectiontoolkit</a>.</p> <br> [1] Ramien, Gregor et al., <a href="https://arxiv.org/abs/1907.12915" rel="nofollow">"Reg R-CNN: Lesion Detection and Grading under Noisy Labels"</a>. In: UNSURE Workshop at MICCAI, 2019.<br> [2] He, Kaiming, et al. <a href="https://arxiv.org/abs/1703.06870" rel="nofollow">"Mask R-CNN"</a> ICCV, 2017<br> <br> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><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 is a comprehensive framework for object detection featuring:</p> <ul dir="auto"> <li>2D + 3D implementations of common object detectors: e.g., Mask R-CNN [2], Retina Net [3], Retina U-Net [4].</li> <li>Modular and light-weight structure ensuring sharing of all processing steps (incl. backbone architecture) for comparability of models.</li> <li>training with bounding box and/or pixel-wise annotations.</li> <li>dynamic patching and tiling of 2D + 3D images (for training and inference).</li> <li>weighted consolidation of box predictions across patch-overlaps, ensembles, and dimensions [4] or standard non-maximum suppression.</li> <li>monitoring + evaluation simultaneously on object and patient level.</li> <li>2D + 3D output visualizations.</li> <li>integration of COCO mean average precision metric [5].</li> <li>integration of MIC-DKFZ batch generators for extensive data augmentation [6].</li> <li>possible evaluation of instance segmentation and/or semantic segmentation by dice scores.</li> </ul> <br> <p dir="auto">[3] Lin, Tsung-Yi, et al. <a href="https://arxiv.org/abs/1708.02002" rel="nofollow">"Focal Loss for Dense Object Detection"</a> TPAMI, 2018.<br> [4] Jaeger, Paul et al. <a href="http://arxiv.org/abs/1811.08661" rel="nofollow"> "Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection" </a>, 2018</p> <p dir="auto">[5] <a href="https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py">https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py</a><br> [6] <a href="https://github.com/MIC-DKFZ/batchgenerators">https://github.com/MIC-DKFZ/batchgenerators</a><br><br></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to cite this code</h2><a id="user-content-how-to-cite-this-code" class="anchor" aria-label="Permalink: How to cite this code" href="#how-to-cite-this-code"><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 the Reg R-CNN publication [1] or the original publication [4] depending on what features you use.</p> <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> <p dir="auto">Setup package in virtual environment</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone https://github.com/MIC-DKFZ/RegRCNN.git. cd RegRCNN virtualenv -p python3.7 regrcnn_env source regrcnn_env/bin/activate python setup.py install"><pre class="notranslate"><code>git clone https://github.com/MIC-DKFZ/RegRCNN.git. cd RegRCNN virtualenv -p python3.7 regrcnn_env source regrcnn_env/bin/activate python setup.py install </code></pre></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Custom Extensions</h5><a id="user-content-custom-extensions" class="anchor" aria-label="Permalink: Custom Extensions" href="#custom-extensions"><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 framework uses two custom mixed C++/CUDA extensions: Non-maximum suppression (NMS) and RoIAlign. Both are adapted from the original pytorch extensions (under torchvision.ops.boxes and ops.roialign). The extensions are automatically compiled from the provided source files under RegRCNN/custom_extensions with above setup.py. However, the extensions need to be compiled specifically for certain GPU architectures. Hence, please ensure that the architectures you need are included in your shell's environment variable <code>TORCH_CUDA_ARCH_LIST</code> before compilation.</p> <p dir="auto">Example: You want to use the modules with the new TITAN RTX GPU, which has Compute Capability 7.5 (Turing Architecture), but sometimes you also want to use it with a TITAN Xp (6.1, Pascal). Before installation you need to <code>export TORCH_CUDA_ARCH_LIST="6.1;7.5"</code>. A link list of GPU model names to Compute Capability can be found here: <a href="https://developer.nvidia.com/cuda-gpus" rel="nofollow">https://developer.nvidia.com/cuda-gpus</a>. Note: If you'd like to import the raw extensions (not the wrapper modules), be sure to import torch first.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Prepare the Data</h2><a id="user-content-prepare-the-data" class="anchor" aria-label="Permalink: Prepare the Data" href="#prepare-the-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">This framework is meant for you to be able to train models on your own data sets.</p> <p dir="auto">In order to include a data set in the framework, create a new folder in RegRCNN/datasets, for instance "example_data". Your data set needs to have a config file in the style of the provided example data sets "lidc" and "toy". It also needs a data loader meeting the same requirements as the provided examples. Likely, you will also need a preprocessing script that transforms your data (once per data set creation, i.e., not a repetitive operation) into a suitable and easily processable format. Important requirements:</p> <ul dir="auto"> <li>The framework expects numpy arrays as data and segmentation ground truth input.</li> <li>Segmentations need to be suited for object detection, i.e., Regions of Interest (RoIs) need to be marked by integers (RoI-ID) in the segmentation volume (0 is background). Corresponding properties of a RoI, e.g., the "class_targets" need to be provided in a separate array or list with (RoI-ID - 1) corresponding to the index of the property in the list (-1 due to zero-indexing). Example: A data volume contains two RoIs. The second RoI is marked in the segmentation by number 2. The "class_targets" info associated with the data volume holds the list [2, 3]. Hence, RoI-ID 2 is assigned class 3.</li> <li>This framework uses a modified version of MIC's batchgenerators' segmentation-to-bounding-box conversion tool. In this version, "class_targets", i.e., object classes start at 1, 0 is reserved for background. Thus, if you use "ConvertSegToBoundingBoxCoordinates" classes in your preprocessed data need to start at 1, not 0.</li> </ul> <p dir="auto">Two example data loaders are provided in RegRCNN/datasets. The way I load data is to have a preprocessing script, which after preprocessing saves the data of whatever data type into numpy arrays (this is just run once). During training / testing, the data loader then loads these numpy arrays dynamically. Please note the data input side is meant to be customized by you according to your own needs and the provided data loaders are merely examples: LIDC has a powerful data loader that handles 2D/3D inputs and is optimized for patch-based training and inference. Due to the large data volumes of LIDC, this loader is slow. The provided toy data set, however, is light weight and a good starting point to get familiar with the framework. It is fully creatable from scratch within a few minutes with RegRCNN/datasets/toy/generate_toys.py.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Execute</h2><a id="user-content-execute" class="anchor" aria-label="Permalink: Execute" href="#execute"><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> <ol dir="auto"> <li> <p dir="auto">Set I/O paths, model and training specifics in the configs file: RegRCNN/datasets/<em>your_dataset</em>/configs.py</p> </li> <li> <p dir="auto">i) Train the model:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python exec.py --mode train --dataset_name your_dataset --exp_dir path/to/experiment/directory "><pre class="notranslate"><code>python exec.py --mode train --dataset_name your_dataset --exp_dir path/to/experiment/directory </code></pre></div> <p dir="auto">This copies snapshots of configs and model to the specified exp_dir, where all outputs will be saved. By default, the data is split into 60% training and 20% validation and 20% testing data to perform a 5-fold cross validation (can be changed to hold-out test set in configs) and all folds will be trained iteratively. In order to train a single fold, specify it using the folds arg:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python exec.py --folds 0 1 2 .... # specify any combination of folds [0-configs.n_cv_splits]"><pre class="notranslate"><code>python exec.py --folds 0 1 2 .... # specify any combination of folds [0-configs.n_cv_splits] </code></pre></div> <p dir="auto">ii) Alternatively, train and test consecutively:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python exec.py --mode train_test --dataset_name your_dataset --exp_dir path/to/experiment/directory "><pre class="notranslate"><code>python exec.py --mode train_test --dataset_name your_dataset --exp_dir path/to/experiment/directory </code></pre></div> </li> <li> <p dir="auto">Run inference:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python exec.py --mode test --exp_dir path/to/experiment/directory "><pre class="notranslate"><code>python exec.py --mode test --exp_dir path/to/experiment/directory </code></pre></div> <p dir="auto">This runs the prediction pipeline and saves all results to exp_dir.</p> </li> <li> <p dir="auto">Additional settings:</p> <ul dir="auto"> <li>Check the args parser in exec.py to see which arguments and modes are available.</li> <li>E.g., you may pass <code>-d</code> or <code>--dev</code> to enable a short development run of the whole train_test procedure (small batch size, only one epoch, two folds, one test patient, etc.).</li> </ul> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Models</h2><a id="user-content-models" class="anchor" aria-label="Permalink: Models" href="#models"><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 framework features models explored in [4] (implemented in 2D + 3D): The proposed Retina U-Net, a simple but effective Architecture fusing state-of-the-art semantic segmentation with object detection,<br><br></p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/retu_figure.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/retu_figure.png" width="50%" style="max-width: 100%;"></a></p><br> also implementations of prevalent object detectors, such as Mask R-CNN, Faster R-CNN+ (Faster R-CNN w\ RoIAlign), Retina Net, Detection U-Net (a U-Net like segmentation architecture with heuristics for object detection.)<br><br><br> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/baseline_figure.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/baseline_figure.png" width="85%" style="max-width: 100%;"></a></p><br> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Training annotations</h2><a id="user-content-training-annotations" class="anchor" aria-label="Permalink: Training annotations" href="#training-annotations"><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 framework features training with pixelwise and/or bounding box annotations. To overcome the issue of box coordinates in data augmentation, we feed the annotation masks through data augmentation (create a pseudo mask, if only bounding box annotations provided) and draw the boxes afterwards.<br><br></p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/annotations.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/annotations.png" width="85%" style="max-width: 100%;"></a></p><br> <p dir="auto">The framework further handles two types of pixel-wise annotations:</p> <ol dir="auto"> <li>A label map with individual ROIs identified by increasing label values, accompanied by a vector containing in each position the class target for the lesion with the corresponding label (for this mode set get_rois_from_seg_flag = False when calling ConvertSegToBoundingBoxCoordinates in your Data Loader). This is usual use case as explained in section "Prepare the data".</li> <li>A binary label map. There is only one foreground class and single lesions are not identified. All lesions have the same class target (foreground). In this case the data loader runs a Connected Component Labelling algorithm to create processable lesion - class target pairs on the fly (for this mode set get_rois_from_seg_flag = True when calling ConvertSegToBoundingBoxCoordinates in your data loader).</li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Prediction pipeline</h2><a id="user-content-prediction-pipeline" class="anchor" aria-label="Permalink: Prediction pipeline" href="#prediction-pipeline"><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 framework provides an inference module, which automatically handles patching of inputs, and tiling, ensembling, and weighted consolidation of output predictions:<br><br><br> <a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/prediction_pipeline.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/prediction_pipeline.png" style="max-width: 100%;"></a><br><br></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Consolidation of predictions</h2><a id="user-content-consolidation-of-predictions" class="anchor" aria-label="Permalink: Consolidation of predictions" href="#consolidation-of-predictions"><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"><h3 tabindex="-1" class="heading-element" dir="auto">Weighted Box Clustering</h3><a id="user-content-weighted-box-clustering" class="anchor" aria-label="Permalink: Weighted Box Clustering" href="#weighted-box-clustering"><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">Multiple predictions of the same image (from test time augmentations, tested epochs and overlapping patches), result in a high amount of boxes (or cubes), which need to be consolidated. In semantic segmentation, the final output would typically be obtained by averaging every pixel over all predictions. As described in [4], <strong>weighted box clustering</strong> (WBC) does this for box predictions:<br></p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/wcs_text.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/wcs_text.png" width="650" style="max-width: 100%;"></a><br><br></p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/wcs_readme.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/wcs_readme.png" width="800" style="max-width: 100%;"></a><br><br></p> <p dir="auto">To enable WBC, set self.clustering = "wbc" in your configs file.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Non-Maximum Suppression</h3><a id="user-content-non-maximum-suppression" class="anchor" aria-label="Permalink: Non-Maximum Suppression" href="#non-maximum-suppression"><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">Test-time predictions can alternatively be aggregated with standard non-maximum suppression. In your configs file, simply set self.clustering = "nms" instead of "wbc".</p> <p dir="auto">As a further alternative you may also choose no test-time aggregation by setting self.clustering = None.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Visualization / Monitoring</h2><a id="user-content-visualization--monitoring" class="anchor" aria-label="Permalink: Visualization / Monitoring" href="#visualization--monitoring"><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 opposition to the original framework, this fork uses tensorboard for monitoring training and validation progress. Since, for now, the framework cannot easily be updated to pytorch >= 1.x, we need third-party package <a href="https://github.com/lanpa/tensorboardX">tensorboardX</a> to use tensorboard with pytorch.</p> <p dir="auto">You can set an applicable choice of implemented metrics like "ap" for Average Precision or "auc" for patient-level ROC-AUC in the configs under self.metrics = [...]. Metrics are then evaluated by evaluator.py and recorded in monitor_metrics. logger.metrics2tboard sends monitor_metrics to your tensorboard logfiles at the end of each epoch. You need to separately start a virtual tensorboard server, pass it your experiment directory (or directories, but it crashes if its more than ~5 experiments) and navigate to the server address. (You can also read up on tensoardboard usage in the original documentation).</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Example:</h3><a id="user-content-example" class="anchor" aria-label="Permalink: Example:" href="#example"><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> <ol dir="auto"> <li>Activate your virtualenv where tensorboard is installed.</li> <li>Start tensorboard server. For instance, your experiment directory is <em>yourexpdir</em>: <br> <code>tensorboard --port 6007 --logdir yourexpdir</code></li> <li>Navigate to <code>localhost:6007</code> in your browser.</li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Output monitoring</h3><a id="user-content-output-monitoring" class="anchor" aria-label="Permalink: Output monitoring" href="#output-monitoring"><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">For qualitative monitoring, example plots are saved to <em>yourexpdir</em>/plots for training and validation and <em>yourexpdir</em>/test/example_plots for testing. Note, that test-time example plots may contain unconsolidated predictions over test-time augmentations, thereby possibly showing many overlapping and/or noisy predictions. You may adapt/use separate file RegRCNN/inference_analysis.py to create clean and nice plots of (consolidated) test-time predictions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Balancing Mechanism of Example Data Loader</h2><a id="user-content-balancing-mechanism-of-example-data-loader" class="anchor" aria-label="Permalink: Balancing Mechanism of Example Data Loader" href="#balancing-mechanism-of-example-data-loader"><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 data loaders of the provided example data sets employ a custom mechanism with the goal of assembling target-balanced batches or training sequences. I.e., the amount of examples shown per target class should be near balance.</p> <p dir="auto">The mechanism creates a sampling-likelihood distribution, as shown below, over all available patients (PIDs). At batch generation, some patients are drawn according to this distribution, others are drawn completely randomly (according to a uniform distribution across all patients). The ratio of uniformly and target-dependently drawn patients is set in your configs file by configs.batch_random_ratio. configs.balance_target determines which targets are considered for the balancing distribution.</p> <p dir="auto">The balancing distribution assigns probabilities s.t. expected occurrences of fg and bg RoIs among all classes are as similar as possible. The balance is naturally limited by multiple RoIs occurring in the same patient (e.g, if each patient has 4 RoIs of class 1 and 1 RoI of class 2 the best balancing ratio achievable is still 4:1). See utils/dataloader_utils.BatchGenerator.balance_target_distribution.</p> <p dir="auto">Experience has shown, that showing at least one foreground example in each batch is most critical, other properties have less impact.</p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/MIC-DKFZ/RegRCNN/blob/master/assets/train_gen.png"><img src="/MIC-DKFZ/RegRCNN/raw/master/assets/train_gen.png" width="800" style="max-width: 100%;"></a><br><br></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Unittests</h2><a id="user-content-unittests" class="anchor" aria-label="Permalink: Unittests" href="#unittests"><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">unittests.py contains some verification and testing procedures, which, however, need you to adjust paths in the TestCase classes before execution. Tests can be used, for instance, to verify if your cross-validation folds have been created correctly, or if separate experiments have the same fold splits.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">License</h1><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><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 framework is published under the <a href="https://github.com/MIC-DKFZ/RegRCNN/blob/master/LICENSE">APACHE 2.0 License</a></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="OqXIinFwjfqxQfVqeQoAx5w1d51HD3SKd5DR0xxoo4OoV/H8yWKRk24NAyJJIPWtFXK9p2vMNPzehu3QnOM1Tw==" /> </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"> This repository holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels. 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can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 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6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>