Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.
Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images
Missouri University of Science and Technology（密苏里科技大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔生物医学通信中心）；U.S. National Library of Medicine（美国国家医学图书馆）；Diagnostic Systems Women’s Health and Cancer（诊断系统女性健康与癌症部门）；Becton Dickinson and Company（碧迪公司）
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
Department of Computer Science and Software Engineering (CSSE)（计算机科学与软件工程系）；Concordia University（Concordia大学）；Concordia Institute for Information Systems Engineering (CIISE)（Concordia信息系统工程研究所）；Department of Building, Civil, and Environmental Engineering（建筑、土木和环境工程系）；University of Montreal Hospital Center (CHUM)（蒙特利尔大学医院中心）；Mila–Quebec AI Institute（Mila-魁北克人工智能研究所）；Khalifa University（Khalifa大学）；McGill University（麦吉尔大学）；Institute for Research in Immunology and Cancer（免疫学与癌症研究所在）
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images
Khalifa University（哈利法大学）；Beihang University（北京航空航天大学）；University of Louisville（路易斯维尔大学）
前列腺癌（PCa）是男性中第二致命的癌症形式，并且可以通过检查Gleason组织的结构表示来进行临床分级。本文提出了\RV{a new method}分割格里森组织\RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).}此外，所提出的方法包括两个主要贡献：1）一个协同的混合膨胀因子和层次分解的潜在空间表示有效格里森组织提取，和2）一个三层的损失函数，可以惩罚不同的语义分割模型，准确地提取高度相关的模式。除此之外，在包含10，516个完整载玻片扫描（约71.7M块）的大规模PCa数据集上对所提出的框架进行了广泛评估，其中它在提取Gleason组织方面优于最先进的方案3.22%（就平均交叉-联合而言），在PCa进展分级方面优于最先进的方案6.91%（就F1评分而言）。
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Nanjing University（南京大学）；Shandong Women’s University（山东女子学院）；Tencent（腾讯）；Suzhou Science and Technology Town Hospital（苏州科技城医院）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）
PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Early Life Imaging department, King's College London, London, UK（早期生命成像部门，伦敦国王学院）；Biomedical Computing department, King's College London, London, UK（生物医学计算部门，伦敦国王学院）；Neuroimaging department, King's College London, London, UK（神经影像部门，伦敦国王学院）；Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany（智能成像实验室，放射学研究院，埃尔兰根大学医院）；Women's Health department, School of Life Course and Population Sciences, King's College London, London, UK（妇女健康部门，生命课程与人口科学学院，伦敦国王学院）
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）；Annalise-AI, Pty, Ltd（Annalise人工智能私人有限公司）；Google Cloud AI（谷歌云人工智能）
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
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）；Guilin University of Electronic Technology（桂林电子科技大学）；Guangdong Provicial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）
Imaging Informatics Division（成像信息学部门）；Bioinformatics Institute（生物信息学研究所）；A*STAR（新加坡科技研究局）；Lucence Diagnostics（Lucence诊断公司）；Institute of Bioengineering and Nanotechnology（生物工程与纳米技术研究所）；Singapore General Hospital（新加坡综合医院）；National Cancer Centre Singapore（新加坡国家癌症中心）；Institute of Molecular and Cell Biology（分子与细胞生物学研究所）；National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Image and Pervasive Access Lab（图像与普适访问实验室）
An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images
School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）；School of Electrical Engineering and Robotics, Queensland University of Technology（昆士兰科技大学电气工程与机器人学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
Cullen College of Engineering, University of Houston（休斯顿大学塞勒恩工程学院）；The University of Texas McGovern Medical School（德克萨斯大学麦格芬医学学院）；National Institute of Neurological Disorders and Stroke（国家神经疾病与中风研究所）
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation
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）
Weakly- and Semi-Supervised Probabilistic Segmentation and Quantification of Ultrasound Needle-Reverberation Artifacts to Allow Better AI Understanding of Tissue Beneath Needles
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT
Hangzhou City University（杭州城市大学）；Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院）；Zhejiang University（浙江大学）
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）
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
University Hospital Essen (AöR)（埃森大学医院）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；West German Cancer Center Essen（埃森西德癌症中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Hannover Medical School（汉诺威医学院）
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）
Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learning
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Xiangya Hospital, Central South University（中南大学湘雅医院）；University of Chinese Academy of Sciences（中国科学院大学）；Central South University（中南大学）
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
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。
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）
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
Image Data Analytics, Informatics IT, Merck \& Co., Inc., West Point, PA 19486, USA Image Data Analytics, Informatics IT, Merck \& Co., Inc., Rahway, NJ 07065, USA
