A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
Northeastern University（东北大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；China Medical University（中国医科大学）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Chengdu University of Information Technology（成都信息工程大学）
In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
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（辽宁省肿瘤医院和研究所）；Heilongjiang University（黑龙江大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学盛京医院）；University of Luebeck（吕贝克大学）
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The Chinese University of Hong Kong（香港中文大学）；the Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；Imsight Medical Technology Co., Ltd.（艾迈医疗科技有限公司）
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
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）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
Hierarchical Classification for Improved Histopathology Image Analysis
School of Electrical Engineering, Korea University, Seoul, Korea（韩国大学电气工程学院）；Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, Korea（韩国天主大学医学院医院病理系）
One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training
Sano Centre for Computational Medicine（萨诺计算医学中心）；AGH University of Krakow（克拉科夫AGH大学）；Institute of Informatics, HES-SO Valais-Wallis（瓦莱州瓦利斯高等专业学院信息学研究所）；Faculty of Medicine, University of Geneva (UNIGE)（日内瓦大学医学院）
Gastric histopathology image segmentation using a hierarchical conditional random field
Northeastern University（东北大学）；MBIE College（MBIE学院）；Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education（教育部医学影像与智能分析工程研究中心）；Microscopic Image and Medical Image Analysis Group（显微图像与医学图像分析组）
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
Rutgers University（罗格斯大学）；SenseBrain Technology Limited LLC（SenseBrain科技有限公司）；Rutgers New Jersey Medical School（罗格斯大学新泽西医学院）；SenseTime Research（商汤科技研究院）；Rutgers Cancer Institute of New Jersey（罗格斯大学新泽西癌症研究所）
CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation
AI VIETNAM Lab（AI VIETNAM实验室）；Portland Community College（波特兰社区学院）；University of Science, VNU-HCM（胡志明市国家大学科学大学）；Hanoi University of Science and Technology（河内科技大学）；Jeonbuk National University（全北国立大学）；Washington University School of Medicine（华盛顿大学医学院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
The Pennsylvania State University（宾夕法尼亚州立大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（国家医学图书馆）；Penn State Health Milton S. Hershey Medical Center（宾夕法尼亚州立大学健康米尔顿·S·赫尔希医学中心）；Penn State College Of Medicine（宾夕法尼亚州立大学医学院）
Stain Based Contrastive Co-training for Histopathological Image Analysis
University of Utah（犹他大学）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Huntsman Cancer Institute, University of Utah（犹他大学亨茨曼癌症研究所）
Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology
Technion - IIT（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）
HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology
Edge Hill University（埃奇希尔大学）；University of Nottingham Malaysia（诺丁汉大学马来西亚分校）；University of Southampton Malaysia（南安普顿大学马来西亚分校）；Cancer Research Malaysia（马来西亚癌症研究中心）
