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arXiv 1910.04473eess.IVcs.CV
Multi-Stage Pathological Image Classification using Semantic Segmentation
The University of Tokyo（东京大学）；RIKEN（理化学研究所）；The Japanese Society of Pathology（日本病理学会）；Kyoto University（京都大学）；Tokyo Medical and Dental University（东京医科齿科大学）；National Institute of Informatics（国立信息学研究所）
Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel resolution considering the available memory capacity. Most of the previous works divide high resolution WSIs into small image patches and separately input them into the model to classify it as a tumor or a normal tissue. However, patch-based classification uses only patch-scale local information but ignores the relationship between neighboring patches. If we consider the relationship of neighboring patches and global features, we can improve the classification performance. In this paper, we propose a new model structure combining the patch-based classification model and whole slide-scale segmentation model in order to improve the prediction performance of automatic pathological diagnosis. We extract patch features from the classification model and input them into the segmentation model to obtain a whole slide tumor probability heatmap. The classification model considers patch-scale local features, and the segmentation model can take global information into account. We also propose a new optimization method that retains gradient information and trains the model partially for end-to-end learning with limited GPU memory capacity. We apply our method to the tumor/normal prediction on WSIs and the classification performance is improved compared with the conventional patch-based method.
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Guanghua School of Management, Peking University（北京大学光华管理学院）；Peking University Third Hospital（北京大学第三医院）
Research Group Ophthalmology, KU Leuven（KU Leuven眼科研究组）；Ophthalmology Department, UZ Leuven（UZ Leuven眼科部）；ESAT-PSI, KU Leuven（KU Leuven ESAT-PSI）；Hasselt University（哈塞尔特大学）；VITO NV
Vanderbilt University（范德堡大学）；Big Data Institute, Central South University（中南大学大数据研究院）；Vanderbilt University Medical Center（范德堡大学医学中心）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
The University of Tokyo（东京大学）；Institute of Science Tokyo（东京科学大学）；Tottori University（鸟取大学）；Biomy Inc.（Biomy公司）；Japanese Red Cross Medical Center（日本红十字会医疗中心）；Kanagawa Cancer Center（神奈川县癌症中心）；Kanagawa Cancer Center Research Institute（神奈川县癌症中心研究所）；National Cancer Center Exploratory Oncology Research & Clinical Trial Center（国立癌症中心探索性肿瘤学研究与临床试验中心）
ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学ARTORG生物医学工程研究中心）；Department of Emergency Medicine, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”急诊医学系）；University Clinic for Pneumonology, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”肺科大学诊所）；Department of Diagnostic, Interventional and Pediatric Radiology, Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”诊断、介入和儿科放射学系）；Bern University Hospital “Inselspital”（伯尔尼大学医院“Inselspital”）；University of Bern（伯尔尼大学）
Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions
University of Electronic Science and Technology of China（电子科技大学）；West China Second University Hospital, Sichuan University（四川大学华西第二医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China（自动化科学与工程学院，华南理工大学，广州510641，中国）；Department of Breast and Thyroid Surgery, the Second Affiliated Hospital, University of South China, Hengyang 421001, China（乳腺甲状腺外科，南方大学第二附属医院，湖南衡阳421001，中国）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China（人工智能与自动化学院，华中科技大学，武汉430074，中国）；School of Informatics, Xiamen University, Xiamen 361005, China（信息学院，厦门大学，厦门361005，中国）；Pazhou Laboratory, Guangzhou 510335, China（琶洲实验室，广州510335，中国）；School of Future Technology, South China University of Technology, Guangzhou 510641, China（未来技术学院，华南理工大学，广州510641，中国）
City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology（香港科技大学）；The First Affiliated Hospital of Wenzhou Medical University（温州医科大学附属第一医院）
Xidian University（西安电子科技大学）；The First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Institute of Pathology, University Hospital Carl Gustav Carus (UKD), TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院病理研究所）；ASGEN GmbH & Co. KG（ASGEN两合有限责任公司）；National Center for Tumor Diseases (NCT), Partner Site Dresden（国家肿瘤疾病中心（德累斯顿合作站点））；ContextVision AB（康泰视伦公司）；Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院医学信息学与生物测量学研究所）；Institute of Bioengineering, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
