In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.
Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation
Dartmouth College（达特茅斯学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Minnesota Gastroenterology PA（明尼苏达胃肠病学协会）；Fairview Southdale Hospital（费尔维尤索斯代尔医院）；University of North Carolina（北卡罗来纳大学）
结直肠息肉的组织学分类在结直肠癌筛查和受影响患者的护理中起着关键作用。一个准确的和自动化的算法分类结直肠息肉的数字化组织病理学幻灯片可以使临床医生和患者受益。使用多机构数据集评估深度神经网络在组织病理学切片图像上进行结直肠息肉分类的性能和可推广性。在这项研究中，我们开发了一种深度神经网络，用于对四种主要的结直肠息肉类型进行分类，即管状腺瘤，管状绒毛状/绒毛状腺瘤，增生性息肉和无蒂锯齿状腺瘤，基于我们的机构Dartmouth-Hitchcock Medical Center（DHMC）的数字化组织病理学切片，新罕布什尔州。我们在DHMC的157张组织病理学切片图像的内部数据集以及来自美国13个州的24个不同机构的238张组织病理学切片图像的外部数据集上评估了深度神经网络。我们在本评估中测量了我们模型的准确性、灵敏度和特异性，并将其性能与从相应病理学实验室检索的护理点的当地病理学家诊断进行了比较。对于内部评估，深度神经网络的平均准确度为93.5%（95% CI 89.6%-97.4%），而当地病理学家的准确度为91.4%（95% CI 87.0%-95.8%）。在外部测试集上，深度神经网络的准确率为87.0%（95% CI 82.7%-91.3%），与当地病理学家的准确率86.6%（95% CI 82.3%-90.9%）相当。如果在临床环境中得到证实，我们的模型可以通过提高结直肠癌筛查…
Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning
PLRI Medical Informatics Institute（PLRI医学信息学研究所）；CAIMed Reserch Center（CAIMed研究中心）；Hannover Medical School（汉诺威医学院）；Computer Science Institute（计算机科学研究所）；Leibniz University Hannover（莱比锡大学汉诺威分校）；Sanford Burnham Prebys Medical Discovery Institute（桑福医疗发现研究所）；Rady Children’s Institute for Genomic Medicine（拉迪儿童基因医学研究所）；University of California San Diego（加州大学圣地亚哥分校）
ERCPMP-Gx: Endoscopic Image and Video Dataset for Morphological, Histopathological, and Genomic Characterization of Colorectal Polyposis
Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Familial & Hereditary Cancers Institute（家族与遗传性癌症研究所）；Shiraz University of Medical Sciences（设拉子医科大学）；DataBioX Research（DataBioX研究院）
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）
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
In-context learning enables multimodal large language models to classify cancer pathology images
National Center for Tumor Diseases (NCT)（国家肿瘤中心）；Heidelberg University Hospital（海德堡大学医院）；Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗赖森纽斯数字健康中心）；Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Medical Faculty Mannheim, Heidelberg University（海德堡大学曼海姆医学院）；University Hospital Aachen（亚琛大学医院）；University Hospital Dresden（德累斯顿大学医院）
Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil（精确与技术科学研究所，弗拉维亚联邦大学-UFV，里奥帕拉纳伊巴-巴西）；Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil（计算系，圣卡洛斯联邦大学，圣卡洛斯-巴西）
Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital（海德堡大学医院病理学研究所应用肿瘤生物学系）；National Center for Hereditary Tumor Syndromes, University Hospital Bonn（波恩大学医院遗传性肿瘤综合征国家中心）；Department of Internal Medicine I, University Hospital Bonn（波恩大学医院第一内科）；Leibniz Institut für Wissensmedien（莱布尼茨知识媒体研究所）
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂与华威郡大学医院）；Hiroshima University Institute of Biomedical and Health Sciences（广岛大学生物医学与健康科学研究所）；Osaka University（大阪大学）；The Alan Turing Institute（阿兰·图灵研究所）
Slide-Level Active Learning Reduces Annotation Burden in H&E images
Institute for Biomedical Informatics, Faculty of Medicine（生物医学信息学研究所，医学学院）；University Hospital Cologne, University of Cologne（科隆大学医院，科隆大学）；Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine（科隆分子医学中心（CMMC），医学学院）；Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne（科隆卓越集群：与衰老相关疾病细胞应激反应（CECAD），科隆大学）；Faculty of Mathematics（数学学院）；Natural Sciences, University of Cologne（自然科学学院，科隆大学）；Institute of Pathology, University Hospital Cologne（病理学研究所，科隆大学医院）
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心-德国环境与健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）
PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National Biomedical Imaging Center, College of Future Technology, Peking University（北京大学未来技术学院国家生物医学成像中心）
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；University of New South Wales（新南威尔士大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；Liaoning Shihua University（辽宁石油化工大学）；University of Luebeck（吕贝克大学）
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗雷森纽斯数字健康中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州））；IBM Research Europe（IBM欧洲研究院）；Centre de Recherche des Cordeliers（科尔得利研究所）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；Assistance Publique-Hôpitaux de Paris（巴黎公共医疗援助中心）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；University Hospital Heidelberg（海德堡大学医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Leeds（利兹大学）
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Technical University Dresden（德累斯顿工业大学）；StratifAI GmbH（StratifAI有限公司）；University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院附属医院）；National Center for Tumor Diseases Dresden (NCT/UCC)（德累斯顿国家肿瘤疾病中心（NCT/UCC））；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；Kepler University Hospital, Johannes Kepler University Linz（约翰内斯·开普勒大学林茨开普勒大学医院）；University of Bern（伯尔尼大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University Hospital Aachen（亚琛大学医院）；European Institute of Oncology IRCCS（欧洲肿瘤研究所IRCCS）；University Hospital Heidelberg（海德堡大学医院）
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）
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University（复旦大学附属中山医院内镜中心及内镜研究所）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
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
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
Division of Computing and Data Science, Ruđer Bošković Institute（鲁德·波斯科维奇研究所计算与数据科学部）；Sitnik Ventures UG（Sitnik Ventures公司）；Department of Pathology and Cytology, Dubrava University Hospital（杜布拉瓦大学医院病理科与细胞学部）；Clinical Division for Pathology and Cytology, Clinical Hospital Center Zagreb（扎格雷布临床医院中心病理科与细胞学部）；Division of Molecular Medicine, Ruđer Bošković Institute（鲁德·波斯科维奇研究所分子医学部）
