Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality, and the role of prompt precise detection is of paramount interest in improving patient outcomes. Conventional diagnostic methods such as colonoscopy and histological examination routinely exhibit subjectivity, are extremely time-consuming, and are susceptible to variation. Through the development of digital pathology, deep learning algorithms have become a powerful approach in enhancing diagnostic precision and efficiency. In our work, we proposed a convolutional neural network architecture named MSRANetV2, specially optimized for the classification of colorectal tissue images. The model employs a ResNet50V2 backbone, extended with residual attention mechanisms and squeeze-and-excitation (SE) blocks, to extract deep semantic and fine-grained spatial features. With channel alignment and upsampling operations, MSRANetV2 effectively fuses multi-scale representations, thereby enhancing the robustness of the classification. We evaluated our model on a five-fold stratified cross-validation strategy on two publicly available datasets: CRC-VAL-HE-7K and NCT-CRC-HE-100K. The proposed model achieved remarkable average Precision, recall, F1-score, AUC, and test accuracy were 0.9884 plus-minus 0.0151, 0.9900 plus-minus 0.0151, 0.9900 plus-minus 0.0145, 0.9999 plus-minus 0.00006, and 0.9905 plus-minus 0.0025 on the 7K dataset. On the 100K dataset, they were 0.9904 plus-minus 0.0091, 0.9900 plus-minus 0.0071, 0.9900 plus-minus 0.0071, 0.9997 plus-minus 0.00016, and 0.9902 plus-minus 0.0006. Additionally, Grad-CAM visualizations were incorporated to enhance model interpretability by highlighting tissue areas that are medically relevant. These findings validate that MSRANetV2 is a reliable, interpretable, and high-performing architectural model for classifying CRC tissues.
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）
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning
Süleyman Demirel University（苏莱曼·德米雷尔大学）；Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Karunya Institute of Technology & Sciences（卡鲁尼亚技术与科学学院）
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）
XDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System In Colorectal Cancer
MedBravo；Alicante University General Hospital（阿利坎特大学综合医院）；Alicante Institute for Health and Biomedical Research（阿利坎特健康与生物医学研究所）；Yale University（耶鲁大学）；Hospital del Mar（马尔医院）；Hospital del Mar Medical Research Institute（马尔医院医学研究所）；Hospital Clínic（医院诊所医院）；University of Barcelona（巴塞罗那大学）
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）
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, 39762, USA（工业与系统工程系，密苏里州立大学）；Department of Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, 39216, USA（放射肿瘤学系，密苏里大学医学中心）
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）
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）
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）；School of Management, University of Science and Technology of China（中国科学技术大学管理学院）；School of Population Health, UNSW Sydney（新南威尔士大学悉尼分校人口健康学院）；Genmab Inc.（杰特贝公司）
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）
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Institute for Cancer Genetics and Informatics（癌症基因组学与信息学研究所）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Department of Gastrointestinal（胃肠科部）；Institute of Clinical Medicine（临床医学研究所）；Department of Gynaecological Oncology（妇科肿瘤科部）；Amsterdam University Medical Centres（阿姆斯特丹大学医学中心）；Department of Obstetrics and Gynaecology（妇产科部）；Comprehensive Cancer Center Innsbruck（因斯布鲁克综合癌症中心）；Innsbruck Medical University（因斯布鲁克医学院）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Department of Medical Biology（医学生物学部）；UiT The Arctic University of Norway（挪威北极大学（UiT））；Department of Clinical Pathology（临床病理学部）
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究所（INESC TEC））；Faculty of Engineering, University of Porto (FEUP)（波尔图大学工程学院）；IMP Diagnostics（IMP诊断公司）；Research Center of IPO Porto (CI-IPOP)（波尔图IPO癌症研究所研究中心（CI-IPOP））；Portuguese Oncology Institute of Porto (IPO Porto)（葡萄牙波尔图癌症研究所）；Porto Comprehensive Cancer Center (Porto.CCC)（波尔图综合癌症中心）；School of Medicine and Biomedical Sciences – University of Porto (ICBAS-UP)（波尔图大学医学与生物医学科学学院（ICBAS-UP））
A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
Department of Computer Science and Engineering（计算机科学与工程系）
High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural Networks
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
German Cancer Research Center (DKFZ)（德国癌症研究中心）；University Heidelberg（海德堡大学）；RWTH Aachen University（亚琛工业大学）；University of Ulm（乌尔姆大学）；University of Saarland（萨尔大学）；University Hospital Düsseldorf（杜塞尔多夫大学医院）；Medical University of Vienna（维也纳医科大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；Martin Luther University Halle-Wittenberg（马丁路德·哈勒维腾贝格大学）；University Hospital Zurich（苏黎世大学医院）；Städtisches Klinikum Karlsruhe（卡尔斯鲁厄市立医院）；University of Leipzig（莱比锡大学）；University Medical Center Freiburg（弗莱堡大学医学中心）
