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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Ikamura, Kenji Gerber, Georg Liang, Ivy Phi Le, Long Ding, Tong Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The field of computational pathology has witnessed remarkable progress in the development of both taskspecific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology using an in-house developed foundational vision encoder pretrained on 100 million histology images from over 100,000 patient cases and 1.18 million pathology image-caption pairs. The vision encoder is then combined with a pretrained large language model and the whole system is finetuned on over 250,000 diverse disease agnostic visual language instructions. We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4. When relevant clinical context is provided with the histology image, PathChat achieved a diagnostic accuracy of 87% on multiple-choice questions based on publicly available cases of diverse tissue origins and disease models. Additionally, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision language AI assistant that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making. © 2023, CC BY-NC-ND.

关键词： Pathology

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A General-Purpose Self-Supervised Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Sex-Specific Effects of Prenatal Exposure to Endocrine Disrupting Chemicals on Adverse Birth Outcomes: Results from the Korean Children's Environmental Health Study (Ko-Chens)

SSRN

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SSRN 2025年

作者： Oh, Jongmin Shah, Surabhi Lee, Kyung A. Park, Eunkyo Lee, Dong-Wook Hong, Yun-Chul Song, Sanghwan Kim, Sung Yeon Park, Huibyeol Ha, Eunhee Kim, Suejin Park, Choonghee Song, Sanghwan Lee, Jueun Park, Huibyeol Jo, JungHeum Jung, A-Ra Yu, Seung Do Kim, Hyun Jeong Jung, Soon-Won Hong, Sooyeon Seo, Heung Won Hwang, Namkyoung Kang, Tack Shin Jeong, Dong Jin Lee, Seungjune Kim, Hwan-Cheol Jeong, Kyoung Sook Kim, Byungmi Lee, Seulbi Kim, Hyesook Kook, Hoon Baek, Hee Jo Moon, Jai Dong Park, Won Ju Shin, Myung-Geun Ha, Mina Paik, Ki-Chung Kwon, Ho-Jang Lim, Myung-Ho Yoo, Seung Jin Bae, Sanghyuk Kim, Jin Kyung Bae, Jisuk Hong, Young-Seoub Kim, Yu-Mi Son, Hyunjin Park, Mi-Hye Lim, Dae Hyun Kim, Jeong Hee Hong, Sung-Chul Lee, Keun Hwa Kim, Su-Young Kim, Woo Jin Na, Sunghun Lim, Myoung-Nam Yoo, Young Song, Dae Jin Lee, Jue Seong Yoon, Wonsuck Choe, Seung-Ah Kim, Seunghyun Choung, Ji Tae Kim, Sukyung Kim, Jihyun Ahn, Kangmo Hong, Yun-Chul Choi, Baek-Yong Ryoo, Seung-Woo Son, Seok-Yoon Lee, Ji-Hyeon Lee, Dong-Wook Woo, KuckHyeun Yoon, Seong Yong Hur, Jongil Choi, Kyung-Hwa Park, Myung-Sook Lee, Seung-Hwa Chu, Yeonhee Kim, Jung-Ah Kim, Yangho Lee, Jiho Sim, Chang Sun Kim, A. Ram Oh, Inbo Department of Environmental Medicine School of Medicine Ewha Womans University Seoul Korea Republic of College of Medicine Ewha Womans University Seoul Korea Republic of Department of Human Systems Medicine College of Medicine Seoul National University Seoul Korea Republic of Department of Obstetrics and Gynecology Ewha Womans University College of Medicine Seoul Korea Republic of Department of Occupational and Environmental Medicine Inha University Hospital Inha University Incheon Korea Republic of Institute of Environmental Medicine Medical Research Center Seoul National University Korea Republic of Environmental Health Research Division National Institute of Environmental Research Incheon Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Ewha Medical Research Institute College of Medicine Seoul Korea Republic of Environmental Health Research Division Department of Environmental Health Research National Institute of Environmental Research Ministry of Environment Incheon Korea Republic of Environmental Health Policy Division Office of Environmental Health Ministry of Environment Sejong Korea Republic of Department of Occupational and Environmental Medicine Inha University School of Medicine Incheon Korea Republic of Department of Occupational and Environmental Medicine Wonju Severance Christian Hospital Wonju College of Medicine Yonsei University Wonju Korea Republic of Division of Cancer Prevention National Cancer Control Institute National Cancer Center Goyang Korea Republic of Department of Big Data Strategy National Health Insurance Service Wonju Korea Republic of Department of Food and Nutrition Wonkwang University Iksan Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Pediatrics Chonnam National University Hwasun Hospital Hwasun Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Occupational and

Objectives Although previous epidemiological studies have reported the effects of fetal exposure to phthalates and phenols on birth outcomes, evidence is limited. The objective of this study was to investigate whether prenatal exposure to endocrine-disrupting chemicals [EDCs;phthalates and bisphenol A (BPA)] is associated with birth outcomes and whether there are sex-specific *** We used data from the Korean CHildren’s ENvironmental health Study (Ko-CHENS) cohort. Exposure to phthalates and BPA was measured by urine tests, and the information on outcomes and confounders was obtained from delivery records, biomarker assessment, and a self-reported questionnaire. We performed multivariate logistic regression to estimate the association between exposure to individual EDCs and each birth outcome. Additionally, to evaluate the effects of EDC mixture exposure, we performed weighted quantile sum regression analysis. Subgroup analysis stratified by maternal age and sex at birth was performed to examine potential differences in *** This study included 2,176 mother-child pairs from the Ko-CHENS. We observed a significant association between the risk of PTB and exposure to BPA during early pregnancy and mono-carboxyoctyl phthalate (MCOP) during late pregnancy, with odds ratios of 1.23 (95% CI: 1.01, 1.49) and 1.29 (95% CI: 1.03, 1.62), respectively. Additionally, there was a positive association between exposure to MCOP during early pregnancy and the risk of LBW (OR: 1.39, 95% CI: 1.03, 1.89). For male infants, exposure to MCOP during early pregnancy was associated with the risk of LBW (OR: 2.44, 95% CI: 1.43, 4.15), and exposure to MCOP during late pregnancy was associated with the risk of PTB (OR 1.45, 95% CI: 1.05, 2.02).Conclusions This study presents quantitative evidence for the association between prenatal exposure to EDCs, especially BPA and MCOP, and PTB/LBW. Moreover, the effect was particularly pronounced for older mothers (≥35 years) an

关键词： Obstetrics

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CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

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Nature immunology 2025年 2025 May 22页

作者： Bokai Zhu Sheng Gao Shuxiao Chen Yuchen Wang Jason Yeung Yunhao Bai Amy Y Huang Yao Yu Yeo Guanrui Liao Shulin Mao Zhenghui G Jiang Scott J Rodig Ka-Chun Wong Alex K Shalek Garry P Nolan Sizun Jiang Zongming Ma Ragon Institute of MGH MIT and Harvard Cambridge MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Massachusetts Institute of Technology Cambridge MA USA. Department of Statistics and Data Science The Wharton School University of Pennsylvania Philadelphia PA USA. Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Computer Science City University of Hong Kong Hong Kong People's Republic of China. Dana-Farber Cancer Institute Boston MA USA. Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou People's Republic of China. Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School Boston MA USA. Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Division of Gastroenterology/Liver Center Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA USA. Department of Pathology Stanford University Stanford CA USA. gnolan@stanford.edu. Broad Institute of MIT and Harvard Cambridge MA USA. sjiang3@bidmc.harvard.edu. Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. sjiang3@bidmc.harvard.edu. Dana-Farber Cancer Institute Boston MA USA. sjiang3@bidmc.harvard.edu. Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA. sjiang3@bidmc.harvard.edu. Department of Statistics and Data Science Yale University New Haven CT USA. zongming.ma@yale.edu.

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells' molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate cancer

arXiv

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arXiv 2023年

作者： Pati, Pushpak Jaume, Guillaume Ayadi, Zeineb Thandiackal, Kevin Bozorgtabar, Behzad Gabrani, Maria Goksel, Orcun IBM Research Europe Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School United States Department of Pathology Massachusetts General Hospital Harvard Medical School United States Cancer Program Broad Institute of Harvard MIT United States Data Science Program Dana-Farber Harvard Cancer Center United States Computer-Assisted Applications in Medicine ETH Zurich Switzerland Signal Processing Laboratory 5 EPFL Switzerland EPFL Switzerland Department of Information Technology Uppsala University Sweden

The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the difficulty of obtaining pixel-level annotations, which are tedious and expensive to obtain for Whole-Slide images (WSI). To remedy this, weakly supervised methods have been developed to exploit the annotations directly available at the image level. However, to our knowledge, none of these techniques is adapted to deal with WSIs. In this paper, we propose WHOLESIGHT, a weakly-supervised method, to simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WHOLESIGHT first constructs a tissue-graph representation of the WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo labels via post-hoc feature attribution. These pseudo labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render class prediction and segmentation for an input WSI. We evaluated WHOLESIGHT on three public prostate cancer WSI datasets. Our method achieved state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we quantify the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. © 2023, CC BY-NC-ND.

关键词： Image segmentation

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EMBEDDING SPACE AUGMENTATION FOR WEAKLY SUPERVISED LEARNING IN WHOLE-SLIDE IMAGES

arXiv

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arXiv 2022年

作者： Zaffar, Imaad Jaume, Guillaume Rajpoot, Nasir Mahmood, Faisal Department of Computer Science University College London United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry United Kingdom The Alan Turing Institute London United Kingdom

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EMBAUGMENTER, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster. © 2022, CC BY.

关键词： Generative adversarial networks

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Artificial Intelligence for Digital and Computational Pathology

arXiv

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

arXiv

引用

arXiv 2023年

作者： Jeon, Hyeongseon Xie, Juan Jeon, Yeseul Jung, Kyeong Joo Gupta, Arkobrato Chang, Won Chung, Dongjun Department of Biomedical Informatics The Ohio State University ColumbusOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. © 2023, CC BY-NC-ND.

关键词： RNA

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