Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images
University of Virginia（弗吉尼亚大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Zambia（赞比亚大学）；Washington University（华盛顿大学）；Aga Khan University（阿迦汗大学）
Rasoul Sali, Sodiq Adewole, Lubaina Ehsan, Lee A. Denson, Paul Kelly, Beatrice C. Amadi, Lori Holtz, Syed Asad Ali, Sean R. Moore, Sana Syed, Donald E. Brown
备注：
accepted at IEEE International Conference on Healthcare Informatics (ICHI 2020)
Deep convolutional neural networks(CNNs) have been successful for a wide range of computer vision tasks, including image classification. A specific area of the application lies in digital pathology for pattern recognition in the tissue-based diagnosis of gastrointestinal(GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model that assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since the hierarchical model restricts classification error to each sub-class, it leads to a more informative model than a flat model. In this paper, we propose to apply the hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of its layers' hierarchical structure. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that the hierarchical model can achieve better results than the flat model for multi-category diagnosis of GI disorders using histopathological images.
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）；Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院）；Liuzhou People's Hospital Affiliated to Guangxi Medical University（广西医科大学附属柳州市人民医院）；The Second Affiliated Hospital of Southern University of Science and Technology（南方科技大学第二附属医院）；Shenzhen Zhengjingda Instrument Co., Ltd.（深圳正景达仪器有限公司）；Chongqing University Affiliated Three Gorges Hospital（重庆大学附属三峡医院）；Shenzhen Shengqiang Technology Co., Ltd.（深圳盛强科技有限公司）；Longgang Central Hospital of Shenzhen（深圳市龙岗中心医院）；Ningbo Clinical Pathology Diagnosis Center（宁波市临床病理诊断中心）
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）
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models
Vietnam Aviation Academy（越南航空学院）；Ho Chi Minh City Open University（胡志明市开放大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）
根据一些医学成像技术，称为苏木精和伊红的乳腺组织病理学图像被认为是癌症诊断的金标准。基于将病理图像（WSI）划分为多个补丁的想法，我们使用窗口[512，512]从左到右滑动，从上到下滑动，每个滑动步骤重叠50%，以增强从ICIAR 2018 Grand Challenge收集的400张图像数据集上的数据。然后利用EfficientNet模型对乳腺癌组织病理图像进行分类识别，将其分为正常、良性、癌、浸润癌四种类型。EfficientNet模型是最近开发的一种模型，它使用一组固定的缩放因子统一缩放网络的宽度，深度和分辨率，这些缩放因子非常适合训练高分辨率的图像。实验结果表明，该模型在训练集和评价集上的分类准确率分别达到98%和93%，具有较强的竞争力。
Texture CNN for Histopathological Image Classification
Universidade Estadual de Ponta Grossa（蓬塔格罗萨州立大学）；Pontificia Universidade Católica do Paraná（巴拉那天主教大学）；Universidade Federal do Paraná（巴拉那联邦大学）；École de Technologie Supérieure（高等技术学院）
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
Effects of annotation granularity in deep learning models for histopathological images
Xi'an Jiao tong University（西安交通大学）；School of Electronic and Information Engineering, Xi'an Jiao tong University（西安交通大学电子与信息工程学院）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Xidian University（西安电子科技大学）
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Aignostics GmbH（艾格诺斯蒂克斯公司）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；German Cancer Consortium (DKTK)（德国癌症联盟）
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）
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）
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
Umm Al-Qura University（乌姆·库拉大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Tabuk（塔布克大学）；King Faisal Specialist Hospital and Research Center（费萨尔国王专科医院和研究中心）；University of Nevada（内华达大学）
PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）；The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Polytechnic Normal University（广东技术师范大学）
GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；Manchester Metropolitan University（曼彻斯特城市大学）；Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国立大学）
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学学院）；Air University（空军大学）；Universiti Teknologi PETRONAS（马来西亚国油科技大学）；Korea National University of Transportation（韩国交通大学）
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）；Chengdu University of Information Technology（成都信息工程大学）
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
Tyris Tech S.L.（泰瑞斯科技公司）；Universitat Politècnica de València（瓦伦西亚理工大学）；ValgrAI – Valencian Graduate School and Research Network for Artificial Intelligence（瓦伦西亚人工智能研究生院与研究网络）
DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Pathology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University（广西医科大学柳州市人民医院病理科）；Department of Immunology, College of Basic Medical Sciences, China Medical University（中国医科大学基础医学学院免疫科）；Greater Bay Area Center for Medical Device Evaluation and Inspection.NMPA（粤港澳大湾区医疗器械评价和检验中心.NMPA）；Shenzhen Shengqiang Technology Co., Ltd.（深圳盛强科技有限公司）；Department of Pathology, Chongqing University Affiliated Three Gorges Hospital（重庆大学附属第三人民医院病理科）
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
