In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues. Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. Within our XM-GAN, a novel controllable fusion block densely aggregates local regions of reference images based on their similarity to those in the base image, resulting in locally consistent features. To the best of our knowledge, we are the first to investigate few-shot generation in colorectal tissue images. We evaluate our few-shot colorectral tissue image generation by performing extensive qualitative, quantitative and subject specialist (pathologist) based evaluations. Specifically, in specialist-based evaluation, pathologists could differentiate between our XM-GAN generated tissue images and real images only 55% time. Moreover, we utilize these generated images as data augmentation to address the few-shot tissue image classification task, achieving a gain of 4.4% in terms of mean accuracy over the vanilla few-shot classifier. Code: \url{https://github.com/VIROBO-15/XM-GAN}
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
University of Oxford（牛津大学）；University Hospitals Coventry and Warwickshire（考文垂与沃里克郡大学医院）；University of Warwick（华威大学）；Hamad Medical Corporation（哈马德医疗公司）；International Association for Cancer Research（国际癌症研究协会）
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
Netherlands Cancer Institute（荷兰癌症研究所）；University of Amsterdam（阿姆斯特丹大学）；Ellogon AI B.V.（Ellogon人工智能有限公司）；Radboud University Medical Center（拉德堡德大学医学中心）
ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Department of Computer Science \& Software Engineering, Concordia University, 2155 Guy St, Montreal, QC H3H 2L9, Canada；Department of Electrical \& Computer Engineering, University of Toronto, 10 King's College Rd, Toronto, ON M5S 3G8, Canada；Department of Chemistry \& Biology, Toronto Metropolitan University, 350 Victoria St. Toronto, ON M5B 2K3, Canada；Department of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Edouard Montpetit Blvd, Montreal, QC H3T 1J4, Canada；Axe Cancer, Centre de recherche du CHUM, 900 Saint-Denis St, Montréal, QC H2X 0A9, Canada；Anatomic Pathology, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada；Department of Laboratory Medicine \& Pathobiology, University of Toronto, Simcoe Hall, 1 King's College Circle, Toronto, ON M5S 3K3, Canada；Department of Pathology \& Molecular Medicine, Queen’s University, 88 Stuart Street Queen's University Kingston, ON K7L 3N6 Canada；Department of Pathology \& Molecular Medicine, Université de Montréal, 2900 Édouard-Montpetit Blvd, Montréal, QC H3T 1J4, Canada
Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel
Computer Science Department, Københavns Universitet (KU)（哥本哈根大学计算机科学系）；University of Southern Denmark（南部丹麦大学）；Odense University Hospital（奥登塞大学医院）；OpenSourceResearch Collaboration（开源研究协作）
Automated risk classification of colon biopsies based on semantic segmentation of histopathology images
Radboud University Medical Center（拉德堡德大学医学中心）；Ospedale Cannizzaro（坎尼扎罗医院）；Leids Universitair Medisch Centrum（莱顿大学医学中心）；University of Bayreuth（拜罗伊特大学）；Linköping University（林雪平大学）
