Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture the intricate interaction features among instances within the local area of WSI. Moreover, existing methods for cancer survival prediction based on WSI often fail to provide better clinically meaningful predictions. To overcome these challenges, we propose a Sparse Context-aware Multiple Instance Learning (SCMIL) framework for predicting cancer survival probability distributions. SCMIL innovatively segments patches into various clusters based on their morphological features and spatial location information, subsequently leveraging sparse self-attention to discern the relationships between these patches with a context-aware perspective. Considering many patches are irrelevant to the task, we introduce a learnable patch filtering module called SoftFilter, which ensures that only interactions between task-relevant patches are considered. To enhance the clinical relevance of our prediction, we propose a register-based mixture density network to forecast the survival probability distribution for individual patients. We evaluate SCMIL on two public WSI datasets from the The Cancer Genome Atlas (TCGA) specifically focusing on lung adenocarcinom (LUAD) and kidney renal clear cell carcinoma (KIRC). Our experimental results indicate that SCMIL outperforms current state-of-the-art methods for survival prediction, offering more clinically meaningful and interpretable outcomes. Our code is accessible at https://github.com/yang-ze-kang/SCMIL.
Mallinckrodt Institute of Radiology, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine（华盛顿大学医学院信息学、数据科学与生物统计学研究所）
Look a Group at Once: Multi-Slide Modeling for Survival Prediction
Sichuan University（四川大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science Technology（香港科技大学）
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）
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
University of Information Technology（信息技术大学）；Viet Nam National University Ho Chi Minh City（胡志明市越南国立大学）；ETIS (UMR 8051), CY Cergy Paris University, ENSEA, CNRS, France（法国CY Cergy巴黎大学、ENSEA、法国国家科学研究中心联合建立的ETIS实验室（UMR 8051））
Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；School of Software, Tsinghua University（清华大学软件学院）；School of Computer Science and Engineering, UNSW Sydney（新南威尔士大学计算机科学与工程学院）
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)
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））
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）
SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers
School of Computer Science University of St Andrews（计算机科学学院圣安德鲁大学）；School of Medicine University of St Andrews（医学学院圣安德鲁大学）；Lothian Biorepository NHS Lothian（洛锡安生物库NHS洛锡安）
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）
ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis
Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China（复旦大学基础医学学院数字医学研究中心）；Shanghai Key Lab of Medical Image Computing（上海医学图像计算重点实验室）
HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction
Sichuan University（四川大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Sydney（悉尼大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Zhejiang University（浙江大学）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
University of Science and Technology Beijing（北京科技大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；Changhai Hospital（长海医院）；Peking University（北京大学）
PTCMIL: Multiple Instance Learning via Prompt Token Clustering for Whole Slide Image Analysis
The University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Chungnam National University（Chungnam国立大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）
本文提出 PTCMIL，利用可学习提示词元、投影聚类、词元合并与原型池化实现端到端 WSI 的 MIL 聚合，在八个数据集上取得优于现有方法的分类与生存分析性能。
Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis
University of Science and Technology of China（中国科学技术大学）；the First Affiliated Hospital of USTC（中国科学技术大学第一附属医院）；Beijing Advanced Innovation Center on Biomedical Engineering（北京生物医学工程高精尖创新中心）
HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Cancer Hospital of Dalian University of Technology, Shenyang, China（大连理工大学沈阳医院）；School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China（大连理工大学生物医学工程学院）；Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, China（大连理工大学集成电路与生物医学电子系统重点实验室）
C$^2$MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis
University of Science and Technology of China（中国科学技术大学）；Xiamen University（厦门大学）；EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales（EuroMov Digital Health in Motion，蒙彼利埃大学，IMT Mines Ales）；The University of Hong Kong（香港大学）
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
Massachusetts Institute of Technology（麻省理工学院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images
Faculty of Science, University of British Columbia（不列颠哥伦比亚大学理学院）；School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Department of Pathology and Laboratory Medicine, University of British Columbia（不列颠哥伦比亚大学病理学与实验医学系）；Canada’s Michael Smith Genome Sciences Centre, BC Cancer Research Institute（加拿大迈克尔·史密斯基因组科学中心，BC癌症研究所）
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction
1 Academy for Engineering；Technology, Fudan University, Shanghai, China；2 Institute of Metaverse \& Intelligent Medicine, Fudan University, Shanghai, China；3 Engineering Research Center of AI；4 Jilin Provincial Key Laboratory of Intelligence Science
MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features
Heilongjiang University（黑龙江大学）；La Trobe University（拉筹伯大学）；University of South China（南华大学）；The First Affiliated Hospital（第一附属医院）；Hengyang Medical School（衡阳医学院）；School of Basic Medical Sciences（基础医学院）
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（俄亥俄州立大学医学学院病理学系，韦克斯纳医学中心）；Center for Artificial Intelligence Research, Wake Forest University School of Medicine（威克森林大学医学院人工智能研究中心）
