Modern histopathological image analysis relies on the segmentation of cell structures to derive quantitative metrics required in biomedical research and clinical diagnostics. State-of-the-art deep learning approaches predominantly apply convolutional layers in segmentation and are typically highly customized for a specific experimental configuration; often unable to generalize to unknown data. As the model capacity of classical convolutional layers is limited by a finite set of learned kernels, our approach uses a graph representation of the image and focuses on the node transitions in multiple magnifications. We propose a novel architecture for semantic segmentation of cell nuclei robust to differences in experimental configuration such as staining and variation of cell types. The architecture is comprised of a novel neuroplastic graph attention network based on residual graph attention layers and concurrent optimization of the graph structure representing multiple magnification levels of the histopathological image. The modification of graph structure, which generates the node features by projection, is as important to the architecture as the graph neural network itself. It determines the possible message flow and critical properties to optimize attention, graph structure, and node updates in a balanced magnification loss. In experimental evaluation, our framework outperforms ensembles of state-of-the-art neural networks, with a fraction of the neurons typically required, and sets new standards for the segmentation of new nuclei datasets.
Prompt-Free Lightweight SAM Adaptation for Histopathology Nuclei Segmentation with Strong Cross-Dataset Generalization
School of Information and Communication Technology, Griffith University, QLD, Australia（信息与通信技术学院，格里菲斯大学，昆士兰，澳大利亚）；School of MDP -- Clinical Medicine, Griffith University, QLD, Australia（MDP临床医学学院，格里菲斯大学，昆士兰，澳大利亚）
Structure Embedded Nucleus Classification for Histopathology Images
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong at Shenzhen（香港中文大学（深圳））；Pazhou Lab（琶洲实验室）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Artificial Intelligence Thrust, The Hong Kong University of Science and Technology at Guangzhou（香港科技大学广州人工智能部）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Guangdong Provincial Key Laboratory of Big Data Computing（广东省大数据计算重点实验室）；Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases（广东省结直肠和盆底疾病重点实验室）
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）
Tissue Aware Nuclei Detection and Classification Model for Histopathology Images
TIA Centre, Department of Computer Science, University of Warwick, UK（沃里克大学计算机科学系TIA中心，英国）；GSK, Artificial Intelligence and Machine Learning, London, UK（葛兰素史克人工智能与机器学习，英国）
AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images
Jadavpur University（jadavpur大学）；Artificial Intelligence Research Institute, China University of Mining and Technology（中国矿业大学人工智能研究院）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡国立电工技术大学"LETI"）
DeNuC: Decoupling Nuclei Detection and Classification in Histopathology
School of Automation and Electrical Engineering, University of Science and Technology Beijing（自动化与电气工程学院，北京科技大学）；University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（工业光谱成像工程研究中心）
Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
Stony Brook University（石溪大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
University of Oslo（奥斯陆大学）；Otto-von-Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Singapore Institute of Technology（新加坡科技学院）；Oslo University Hospital（奥斯陆大学医院）；UiT The Arctic University of Norway（挪威北极大学）
A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
Radboud University Medical Center（拉德堡德大学医学中心）；University of Brescia（布雷西亚大学）；Ospedale Sacro Cuore（圣十字医院）；Provincial Hospital of Bolzano (SABES-ASDAA)（博尔扎诺省医院）；University and Hospital Trust of Verona（威尼斯大学与医院信托）；Karolinska University Hospital（卡罗林斯卡大学医院）；Biopticka Laboratory, Ltd（Biopticka实验室）
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning
KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
University of Warwick（华威大学）；VSION Lab, Department of Computer Science, University of Warwick（华威大学计算机科学系VSION实验室）；Tissue Image Analytics (TIA) Centre, Department of Computer Science, University of Warwick（华威大学计算机科学系组织图像分析中心）；Department of Cellular Pathology, University Hospitals of Derby and Burton NHS Foundation Trust（德比和伯顿NHS基金会信托大学医院细胞病理科）
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；University of Pittsburgh（匹兹堡大学）；Microsoft Corporation（微软公司）
GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation
University of Missouri（密苏里大学）；Lander University（兰德大学）；Sagarmatha Engineering College, Tribhuvan University（萨加玛塔工程学院，特里布万大学）；Morsani College of Medicine, University of South Florida（莫尔萨尼医学院，南佛罗里达大学）
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Shenzhen Research Institute of Big Data（深圳市大数据研究院）；School of Computer Science and Engineering, Research Institute of Sun Yat-sen University in Shenzhen, Sun Yat-sen University（中山大学计算机科学与工程学院，中山大学深圳研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Health Development Research and Data Management Center（深圳市卫生健康发展研究和数据管理中心）
AttriPrompter: Auto-Prompting with Attribute Semantics for Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Beihang University（北京航空航天大学）；ByteDance Inc.（字节跳动公司）；Zhejiang University（浙江大学）；Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医学院中国医学科学院肿瘤医院）
Institute of Imaging and Computer Vision, RWTH Aachen University（亚琛工业大学成像与计算机视觉研究所）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Institute of Medical Sciences, Kermanshah University（克尔曼沙阿大学医学研究所）
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；Nanjing Drum Tower Hospital（南京鼓楼医院）；Affiliated Hospital of Nanjing University of Chinese Medicine（南京中医药大学附属医院）
University Medicine Göttingen, Department of General, Visceral and Pediatric Surgery（乌尔姆医学院，普通外科、 visceral 外科和儿童外科部门）；Georg-August-University Göttingen, Institute of Computer Science（哥廷根大学，计算机科学学院）；CAIMed - Lower Saxony Center for AI & Causal Methods in Medicine, Göttingen（下萨克森AI与医学因果方法研究中心，哥廷根）；Cluster of Excellence Multiscale Bioimaging (MBExC), Georg-August-University Göttingen（多尺度生物成像集群（MBExC），哥廷根大学）
CellGenNet: A Knowledge-Distilled Framework for Robust Cell Segmentation in Cancer Tissues
a College of Computing, Georgia Institute of Technology, Atlanta, GA, USA b Department of Pediatrics, Emory University, Atlanta, GA, USA c Departments of Radiology \& Imaging Sciences, Emory University, Atlanta, GA, USA d Department of Biomedical Engineering, Emory University \& Georgia Institute of Technology, Atlanta, GA, USA e School of Electrical
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂与华威郡大学医院）；Hiroshima University Institute of Biomedical and Health Sciences（广岛大学生物医学与健康科学研究所）；Osaka University（大阪大学）；The Alan Turing Institute（阿兰·图灵研究所）
