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arXiv 2107.13048eess.IVcs.CVq-bio.TO
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）
Richard J. Chen, Ming Y. Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://github.com/mahmoodlab/Patch-GCN.
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
National University of Singapore；National University of Singapore Guangzhou Research Translation；Innovation Institute；Imperial College London；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences \& Peking Union Medical College；Hunan University；Institute of High Performance Computing, Agency for Science, Technology；Research (A STAR)
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
Imperial College London, Department of Electrical and Electronic Engineering, London, UK（帝国理工学院伦敦分校，电子与电气工程系）；Department of Pathology, Institut de Pathologie Multisite, Groupement Hospitalier Sud, Lyon University Hospital, Pierre-Bénite, France（病理学系，多站点病理研究所，南部医院集团，里昂大学医院，皮埃尔-贝内蒂，法国）；University of Lyon, Université Claude Bernard Lyon 1, Lyon, France（里昂大学， Claude Bernard 里昂第一大学，里昂，法国）
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）理学院）
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
Nanjing University of Information Science and Technology（南京信息工程大学）；Case Western Reserve University（凯斯西储大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Nanjing Women and Children's Healthcare Hospital（南京市妇幼保健院）；Guangdong Provincial People's Hospital（广东省人民医院）；Nanjing University of Science and Technology（南京理工大学）；Lishui Hospital of Zhejiang University（浙江大学丽水医院）
University of Toronto（多伦多大学）；University of British Columbia（不列颠哥伦比亚大学）；Sunnybrook Health Science Center（森尼布鲁克健康科学中心）；Sunnybrook Research Institute（森尼布鲁克研究所）
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）
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）
