Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network
Institute for Regeneration and Repair, University of Edinburgh（再生与修复研究所，爱丁堡大学）；School of Informatics, University of Edinburgh（信息学院，爱丁堡大学）；Indica Labs, 8700 Education Pl NW, Bldg. B Albuquerque, US（Indica实验室，美国阿尔伯克基8700教育大道西北区B座）；Medical School, University of St Andrews（医学学校，圣安德鲁大学）
Shuyang Wu, Yifu Qiu, Ines P Nearchou, Sandrine Prost, Jonathan A Fallowfield, Hakan Bilen, Timothy J Kendall
Multiple-instance Learning (MIL) is commonly used for computational pathology (CPath), where multi-scale features are essential for capturing both fine cellular details and broad tissue architecture. However, existing multi-scale MIL approaches typically rely on the inflexible multi-magnification inputs or the computationally expensive architectures. As pre-trained foundation models (FMs) become the trend for feature extraction and boost lightweight models, we rethink and explore a more efficient multi-scale MIL method. In this paper, we propose the Multi-scale Pyramidal Network (MSPN), a plug-and-play module for attention-based MIL. MSPN introduces progressive multi-scale whole-slide image analysis using only a single high-magnification input. It consists of (1) grid-based remapping that aggregates high-magnification features to derive spatially-aware coarse feature maps, and (2) the Coarse Guidance Network (CGN) that learns coarse contexts. We benchmark MSPN as an add-on module to 4 attention-based frameworks on 5 clinically relevant tasks with 2 foundation models, and a pre-trained MIL framework. Our results demonstrate that MSPN consistently improves MIL across the compared configurations and tasks, while being lightweight and easy-to-use.
School of Biomedical Engineering（生物医学工程学院）；Department of Pathology and Laboratory Medicine（病理与检验医学系）；Vancouver Prostate Centre（温哥华前列腺中心）；BC Cancer Institute（不列颠哥伦比亚省癌症研究所）；The University of British Columbia（不列颠哥伦比亚大学）
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co., Ltd.（Imsight医疗科技有限公司）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
DCMIL: A Progressive Representation Learning of Whole Slide Images for Cancer Prognosis Analysis
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；Department of Pathology and Institute of Molecular Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University（病理学系和分子病理学研究所，江西医学院第一附属医院，南昌大学）；Guangdong Provincial Key Laboratory of Medical Image Processing（广东省医学图像处理重点实验室）；Department of Biostatistics and Health Data Science, School of Medicine, Indiana University（生物统计学与健康数据科学系，医学学院，印第安纳大学）；Department of Medical and Molecular Genetics, School of Medicine, Indiana University（医学与分子遗传学系，医学学院，印第安纳大学）
PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA Scientific Computing \& Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA Instapath Inc., Houston, TX 77021, USA Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, USA
University of Warwick（华威大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；The Alan Turing Institute（艾伦·图灵研究所）；University Hospitals Coventry & Warwickshire（考文垂与沃里克郡大学医院）
Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
School of Computer Science, Peking University, Beijing, China（北京大学计算机科学系）；Center for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia（健康信息学中心，澳大利亚健康创新研究所，麦考利大学，悉尼，NSW 2113，澳大利亚）；School of Data Science, University of Virginia, Charlottesville, VA, USA（数据科学学院，弗吉尼亚大学，夏洛特维尔，VA，美国）
PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Chair of Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序讲席）；Juntendo University Graduate School of Medicine（顺天堂大学医学院）；Dalian University of Technology（大连理工大学）；University of Science and Technology of China（中国科学技术大学）；Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学慕尼黑数据科学研究所）
GCUNet: A GNN-Based Contextual Learning Network for Tertiary Lymphoid Structure Semantic Segmentation in Whole Slide Image
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；Clinical Oncology School of Fujian Medical University（福建医科大学临床肿瘤学院）；Fujian Cancer Hospital（福建省肿瘤医院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logic
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Research Center for Industries of the Future and School of Engineering, Westlake University, China（未来产业研究中心和西湖大学工程学院）；Department of Computer Science and Engineering, The Ohio State University, USA（俄亥俄州立大学计算机科学与工程系）
HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peking University（北京大学）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
School of Computer Science（计算机科学学院）；Technology, Xi’an Jiaotong University, Xi’an, China（技术学院，西安交通大学，西安，中国）；Department of Transmedia Art, Xi’an Academy of Fine Arts, Xi’an, China（多媒体艺术系，西安美术学院，西安，中国）；Department of Oncology, University of Cambridge, Cambridge, U.K.（肿瘤学系，剑桥大学，剑桥，英国）；Language Technology Lab, University of Cambridge, Cambridge, U.K.（语言技术实验室，剑桥大学，剑桥，英国）；Institute of High Performance Computing, Agency for Science, Technology（高性能计算研究所，科技研究局）
3DPyranet Features Fusion for Spatio-temporal Feature Learning
CVPR Lab, University of Naples ’Parthenope’, Naples, 80143, Italy（CVPR实验室，那不勒斯“帕尔泰诺”大学，意大利那不勒斯）；Department of Computer Science, University of Milan, Milan, Italy（计算机科学系，米兰大学，意大利米兰）；School of Computer Science, University of Galway, Galway, Ireland（计算机科学学院，加里维大学，爱尔兰加里维）
Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images
Vanderbilt University（范德堡大学）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Vanderbilt University Medical Center（范德堡大学医学中心）；Veterans Affairs Tennessee Valley Healthcare System（退伍军人事务部田纳西谷医疗系统）
M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation
IIAU-Lab, Dalian University of Technology（大连理工大学IIAU-Lab）；Zhongshan Hospital of Dalian University（大连大学附属中山医院）；Yale University（耶鲁大学）；Nanyang Technological University（南洋理工大学）
