Hypergraph-based Multi-instance Contrastive Reinforcement Learning for Annotation-free Pan-cancer Survival Prediction on Whole Slide Histology Images
Cheng Lu(Shenzhen Institute of Information Technology), Zaiyi Liu(Key Laboratory of Guangdong Province), Zhenhui Li(Kunming Medical University), Yanfen Cui(Shanxi Provincial Cancer Hospital), Anant Madabhushi(Emory University), Yingqiu Huo(North West Agriculture and Forestry University), Shuting Chen(Guangdong Academy of Medical Sciences), Jiahui Ma(University of Southern California), Wei Zhao(Ningxia University), Zhiyang Chen(Guangdong Academy of Medical Sciences), Ke Zhao(Tangshan College), Li‐Xu Yan(Sun Yat-sen University), Huan Lin(Guangdong Academy of Medical Sciences), Jinglei Tang(Inner Mongolia Agricultural University), Yun Zhu(Kunming Medical University), Guangjun Yang(Kunming Medical University), Xiuming Zhang(Beijing Institute of Technology), Chao Tang(North Sichuan Medical University), Dacheng Yang(Guangdong Academy of Medical Sciences), Jun Liu(Sun Yat-sen University), Wenfeng He(Macau University of Science and Technology), Xiangtian Zhao(Guangdong Academy of Medical Sciences), Yao Suo(Guangdong Academy of Medical Sciences), Hongbo Liu(Hebei University of Engineering), Zhishun Liu(Chinese Academy of Medical Sciences & Peking Union Medical College)
Cited by 1
Related Papers
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
|Nature Reviews Clinical Oncology|2021|810
DRN
|Unknown|2018|633
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
|Medical Image Analysis|2017|489
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update
|Radiology Artificial Intelligence|2024|406