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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13120">arXiv:2411.13120</a> <span> [<a href="https://arxiv.org/pdf/2411.13120">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+L">Luzhe Huang</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Migas%2C+L+G">Lukasz G. Migas</a>, <a href="/search/cs?searchtype=author&query=Van+de+Plas%2C+R">Raf Van de Plas</a>, <a href="/search/cs?searchtype=author&query=Spraggins%2C+J+M">Jeffrey M. Spraggins</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13120v1-abstract-short" style="display: inline;"> Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level spatial resolution and lack cellular mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13120v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13120v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13120v1-abstract-full" style="display: none;"> Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level spatial resolution and lack cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging and advanced image registration steps to enable molecular distributions to be linked to specific tissue features and cell types. Here, we present a virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue using a diffusion model. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with Periodic Acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite utilizing IMS data with 10-fold larger pixel size. Additionally, our approach employs an optimized noise sampling technique during the diffusion model's inference process to reduce variance in the generated images, yielding reliable and repeatable virtual staining. We believe this virtual staining method will significantly expand the applicability of IMS in life sciences and open new avenues for mass spectrometry-based biomedical research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13120v1-abstract-full').style.display = 'none'; document.getElementById('2411.13120v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 Pages, 6 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20073">arXiv:2410.20073</a> <span> [<a href="https://arxiv.org/pdf/2410.20073">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Super-resolved virtual staining of label-free tissue using diffusion models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+L">Luzhe Huang</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+H">Hanlong Chen</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20073v1-abstract-short" style="display: inline;"> Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process to enhance both the spatial resolution and fidelity of label-free virtual tissue staining, addressing the li… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20073v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20073v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20073v1-abstract-full" style="display: none;"> Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process to enhance both the spatial resolution and fidelity of label-free virtual tissue staining, addressing the limitations of traditional deep learning-based methods. Our approach integrates novel sampling techniques into a diffusion model-based image inference process to significantly reduce the variance in the generated virtually stained images, resulting in more stable and accurate outputs. Blindly applied to lower-resolution auto-fluorescence images of label-free human lung tissue samples, the diffusion-based super-resolution virtual staining model consistently outperformed conventional approaches in resolution, structural similarity and perceptual accuracy, successfully achieving a super-resolution factor of 4-5x, increasing the output space-bandwidth product by 16-25-fold compared to the input label-free microscopy images. Diffusion-based super-resolved virtual tissue staining not only improves resolution and image quality but also enhances the reliability of virtual staining without traditional chemical staining, offering significant potential for clinical diagnostics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20073v1-abstract-full').style.display = 'none'; document.getElementById('2410.20073v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 Pages, 5 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.17557">arXiv:2410.17557</a> <span> [<a href="https://arxiv.org/pdf/2410.17557">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> BlurryScope: a cost-effective and compact scanning microscope for automated HER2 scoring using deep learning on blurry image data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fanous%2C+M+J">Michael John Fanous</a>, <a href="/search/cs?searchtype=author&query=Seybold%2C+C+M">Christopher Michael Seybold</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+H">Hanlong Chen</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.17557v1-abstract-short" style="display: inline;"> We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. BlurryScope integrates specialized hardware with a neural network-based model to quickly process motion-blurred histological images and perform automated patho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17557v1-abstract-full').style.display = 'inline'; document.getElementById('2410.17557v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.17557v1-abstract-full" style="display: none;"> We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. BlurryScope integrates specialized hardware with a neural network-based model to quickly process motion-blurred histological images and perform automated pathology classification. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size/weight, making it ideal for fast triaging in small clinics, as well as for resource-limited settings. To demonstrate the proof-of-concept of BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. We evaluated this approach by scanning HER2-stained tissue microarrays (TMAs) at a continuous speed of 5 mm/s, which introduces bidirectional motion blur artifacts. These compromised images were then used to train our network models. Using a test set of 284 unique patient cores, we achieved blind testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+ , 2+/3+) HER2 score classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping of regions of interest, as well as HER2 score classification. We believe BlurryScope has the potential to enhance the current pathology infrastructure in resource-scarce environments, save diagnostician time and bolster cancer identification and classification across various clinical environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17557v1-abstract-full').style.display = 'none'; document.getElementById('2410.17557v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 Pages, 6 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05255">arXiv:2409.05255</a> <span> [<a href="https://arxiv.org/pdf/2409.05255">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Label-free evaluation of lung and heart transplant biopsies using virtual staining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+T">Tairan Liu</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+G">Guangdong Ma</a>, <a href="/search/cs?searchtype=author&query=Qi%2C+Y">Yuxuan Qi</a>, <a href="/search/cs?searchtype=author&query=de+Haan%2C+K">Kevin de Haan</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+X">Xilin Yang</a>, <a href="/search/cs?searchtype=author&query=Correa%2C+A+J">Adrian J. Correa</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+G">Guangqian Xiao</a>, <a href="/search/cs?searchtype=author&query=Jen%2C+K">Kuang-Yu Jen</a>, <a href="/search/cs?searchtype=author&query=Iczkowski%2C+K+A">Kenneth A. Iczkowski</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yulun Wu</a>, <a href="/search/cs?searchtype=author&query=Wallace%2C+W+D">William Dean Wallace</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05255v1-abstract-short" style="display: inline;"> Organ transplantation serves as the primary therapeutic strategy for end-stage organ failures. However, allograft rejection is a common complication of organ transplantation. Histological assessment is essential for the timely detection and diagnosis of transplant rejection and remains the gold standard. Nevertheless, the traditional histochemical staining process is time-consuming, costly, and la… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05255v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05255v1-abstract-full" style="display: none;"> Organ transplantation serves as the primary therapeutic strategy for end-stage organ failures. However, allograft rejection is a common complication of organ transplantation. Histological assessment is essential for the timely detection and diagnosis of transplant rejection and remains the gold standard. Nevertheless, the traditional histochemical staining process is time-consuming, costly, and labor-intensive. Here, we present a panel of virtual staining neural networks for lung and heart transplant biopsies, which digitally convert autofluorescence microscopic images of label-free tissue sections into their brightfield histologically stained counterparts, bypassing the traditional histochemical staining process. Specifically, we virtually generated Hematoxylin and Eosin (H&E), Masson's Trichrome (MT), and Elastic Verhoeff-Van Gieson (EVG) stains for label-free transplant lung tissue, along with H&E and MT stains for label-free transplant heart tissue. Subsequent blind evaluations conducted by three board-certified pathologists have confirmed that the virtual staining networks consistently produce high-quality histology images with high color uniformity, closely resembling their well-stained histochemical counterparts across various tissue features. The use of virtually stained images for the evaluation of transplant biopsies achieved comparable diagnostic outcomes to those obtained via traditional histochemical staining, with a concordance rate of 82.4% for lung samples and 91.7% for heart samples. Moreover, virtual staining models create multiple stains from the same autofluorescence input, eliminating structural mismatches observed between adjacent sections stained in the traditional workflow, while also saving tissue, expert time, and staining costs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05255v1-abstract-full').style.display = 'none'; document.getElementById('2409.05255v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 Pages, 5 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.12337">arXiv:2407.12337</a> <span> [<a href="https://arxiv.org/pdf/2407.12337">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Isil%2C+C">Cagatay Isil</a>, <a href="/search/cs?searchtype=author&query=Koydemir%2C+H+C">Hatice Ceylan Koydemir</a>, <a href="/search/cs?searchtype=author&query=Eryilmaz%2C+M">Merve Eryilmaz</a>, <a href="/search/cs?searchtype=author&query=de+Haan%2C+K">Kevin de Haan</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Mentesoglu%2C+K">Koray Mentesoglu</a>, <a href="/search/cs?searchtype=author&query=Unal%2C+A+F">Aras Firat Unal</a>, <a href="/search/cs?searchtype=author&query=Rivenson%2C+Y">Yair Rivenson</a>, <a href="/search/cs?searchtype=author&query=Chandrasekaran%2C+S">Sukantha Chandrasekaran</a>, <a href="/search/cs?searchtype=author&query=Garner%2C+O+B">Omai B. Garner</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.12337v1-abstract-short" style="display: inline;"> Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free ba… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12337v1-abstract-full').style.display = 'inline'; document.getElementById('2407.12337v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12337v1-abstract-full" style="display: none;"> Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained deep neural network that digitally transforms darkfield images of unstained bacteria into their Gram-stained equivalents matching brightfield image contrast. After a one-time training effort, the virtual Gram staining model processes an axial stack of darkfield microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of the virtual Gram staining workflow on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the virtual Gram staining model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacteria staining framework effectively bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12337v1-abstract-full').style.display = 'none'; document.getElementById('2407.12337v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 Pages, 5 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.18458">arXiv:2404.18458</a> <span> [<a href="https://arxiv.org/pdf/2404.18458">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+L">Luzhe Huang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Haran%2C+T+K">Tal Keidar Haran</a>, <a href="/search/cs?searchtype=author&query=Wallace%2C+W+D">William Dean Wallace</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.18458v1-abstract-short" style="display: inline;"> Histopathological staining of human tissue is essential in the diagnosis of various diseases. The recent advances in virtual tissue staining technologies using AI alleviate some of the costly and tedious steps involved in the traditional histochemical staining process, permitting multiplexed rapid staining of label-free tissue without using staining reagents, while also preserving tissue. However,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18458v1-abstract-full').style.display = 'inline'; document.getElementById('2404.18458v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.18458v1-abstract-full" style="display: none;"> Histopathological staining of human tissue is essential in the diagnosis of various diseases. The recent advances in virtual tissue staining technologies using AI alleviate some of the costly and tedious steps involved in the traditional histochemical staining process, permitting multiplexed rapid staining of label-free tissue without using staining reagents, while also preserving tissue. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical utility of these approaches. Quality assessment of histology images is generally performed by human experts, which can be subjective and depends on the training level of the expert. Here, we present an autonomous quality and hallucination assessment method (termed AQuA), mainly designed for virtual tissue staining, while also being applicable to histochemical staining. AQuA achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to ground truth, also presenting an agreement of 98.5% with the manual assessments made by board-certified pathologists. Besides, AQuA achieves super-human performance in identifying realistic-looking, virtually stained hallucinatory images that would normally mislead human diagnosticians by deceiving them into diagnosing patients that never existed. We further demonstrate the wide adaptability of AQuA across various virtually and histochemically stained tissue images and showcase its strong external generalization to detect unseen hallucination patterns of virtual staining network models as well as artifacts observed in the traditional histochemical staining workflow. This framework creates new opportunities to enhance the reliability of virtual staining and will provide quality assurance for various image generation and transformation tasks in digital pathology and computational imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18458v1-abstract-full').style.display = 'none'; document.getElementById('2404.18458v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 Pages, 7 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00837">arXiv:2404.00837</a> <span> [<a href="https://arxiv.org/pdf/2404.00837">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.34133/bmef.0048">10.34133/bmef.0048 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Selcuk%2C+S+Y">Sahan Yoruc Selcuk</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+X">Xilin Yang</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+B">Bijie Bai</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Aydin%2C+M">Musa Aydin</a>, <a href="/search/cs?searchtype=author&query=Unal%2C+A+F">Aras Firat Unal</a>, <a href="/search/cs?searchtype=author&query=Gomatam%2C+A">Aditya Gomatam</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+Z">Zhen Guo</a>, <a href="/search/cs?searchtype=author&query=Angus%2C+D+M">Darrow Morgan Angus</a>, <a href="/search/cs?searchtype=author&query=Kolodney%2C+G">Goren Kolodney</a>, <a href="/search/cs?searchtype=author&query=Atlan%2C+K">Karine Atlan</a>, <a href="/search/cs?searchtype=author&query=Haran%2C+T+K">Tal Keidar Haran</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00837v1-abstract-short" style="display: inline;"> Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemically (IHC) stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00837v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00837v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00837v1-abstract-full" style="display: none;"> Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Accurate assessment of immunohistochemically (IHC) stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in IHC-stained BC tissue images. Our approach analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. This method addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Our automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might significantly impact cancer treatment planning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00837v1-abstract-full').style.display = 'none'; document.getElementById('2404.00837v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 Pages, 7 Figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> BME Frontiers (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09100">arXiv:2403.09100</a> <span> [<a href="https://arxiv.org/pdf/2403.09100">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41467-024-52263-z">10.1038/s41467-024-52263-z <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+X">Xilin Yang</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+B">Bijie Bai</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Aydin%2C+M">Musa Aydin</a>, <a href="/search/cs?searchtype=author&query=Selcuk%2C+S+Y">Sahan Yoruc Selcuk</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+Z">Zhen Guo</a>, <a href="/search/cs?searchtype=author&query=Fishbein%2C+G+A">Gregory A. Fishbein</a>, <a href="/search/cs?searchtype=author&query=Atlan%2C+K">Karine Atlan</a>, <a href="/search/cs?searchtype=author&query=Wallace%2C+W+D">William Dean Wallace</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.09100v1-abstract-short" style="display: inline;"> Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a birefringence pattern under polarized lig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09100v1-abstract-full').style.display = 'inline'; document.getElementById('2403.09100v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09100v1-abstract-full" style="display: none;"> Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a birefringence pattern under polarized light microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in the amount of amyloid, staining quality and expert interpretation through manual examination of tissue under a polarization microscope. Here, we report the first demonstration of virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue sections into brightfield and polarized light microscopy equivalent images, matching the histochemically stained versions of the same samples. We demonstrate the efficacy of our method with blind testing and pathologist evaluations on cardiac tissue where the virtually stained images agreed well with the histochemically stained ground truth images. Our virtually stained polarization and brightfield images highlight amyloid birefringence patterns in a consistent, reproducible manner while mitigating diagnostic challenges due to variations in the quality of chemical staining and manual imaging processes as part of the clinical workflow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09100v1-abstract-full').style.display = 'none'; document.getElementById('2403.09100v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 Pages, 5 Figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature Communications (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.00920">arXiv:2308.00920</a> <span> [<a href="https://arxiv.org/pdf/2308.00920">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41467-024-46077-2">10.1038/s41467-024-46077-2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Virtual histological staining of unlabeled autopsy tissue </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jingxi Li</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+T">Tairan Liu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+D">Di Wu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+S">Songyu Sun</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+G">Guangdong Ma</a>, <a href="/search/cs?searchtype=author&query=de+Haan%2C+K">Kevin de Haan</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+L">Luzhe Huang</a>, <a href="/search/cs?searchtype=author&query=Hamidi%2C+S">Sepehr Hamidi</a>, <a href="/search/cs?searchtype=author&query=Urisman%2C+A">Anatoly Urisman</a>, <a href="/search/cs?searchtype=author&query=Haran%2C+T+K">Tal Keidar Haran</a>, <a href="/search/cs?searchtype=author&query=Wallace%2C+W+D">William Dean Wallace</a>, <a href="/search/cs?searchtype=author&query=Zuckerman%2C+J+E">Jonathan E. Zuckerman</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.00920v1-abstract-short" style="display: inline;"> Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00920v1-abstract-full').style.display = 'inline'; document.getElementById('2308.00920v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.00920v1-abstract-full" style="display: none;"> Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00920v1-abstract-full').style.display = 'none'; document.getElementById('2308.00920v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 Pages, 7 Figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature Communications (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.06822">arXiv:2211.06822</a> <span> [<a href="https://arxiv.org/pdf/2211.06822">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41377-023-01104-7">10.1038/s41377-023-01104-7 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning-enabled Virtual Histological Staining of Biological Samples </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bai%2C+B">Bijie Bai</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+X">Xilin Yang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuzhu Li</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yijie Zhang</a>, <a href="/search/cs?searchtype=author&query=Pillar%2C+N">Nir Pillar</a>, <a href="/search/cs?searchtype=author&query=Ozcan%2C+A">Aydogan Ozcan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.06822v1-abstract-short" style="display: inline;"> Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and traine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.06822v1-abstract-full').style.display = 'inline'; document.getElementById('2211.06822v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.06822v1-abstract-full" style="display: none;"> Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.06822v1-abstract-full').style.display = 'none'; document.getElementById('2211.06822v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 Pages, 7 Figures, 2 Tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Light: Science & Applications (2023) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 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