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Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images

文献类型：期刊论文

作者Yan, Rui3,4,5,6; Lv, Zhilong4; Yang, Zhidong4; Lin, Senlin4; Zheng, Chunhou2; Zhang, Fa1
刊名IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版日期2024
卷号28期号:1页码:7-18
关键词Hierarchical representation pathological image analysis sparse transformer survival analysis
ISSN号2168-2194
DOI10.1109/JBHI.2023.3307584
英文摘要The Transformer-based methods provide a good opportunity for modeling the global context of gigapixel whole slide image (WSI), however, there are still two main problems in applying Transformer to WSI-based survival analysis task. First, the training data for survival analysis is limited, which makes the model prone to overfitting. This problem is even worse for Transformer-based models which require large-scale data to train. Second, WSI is of extremely high resolution (up to 150,000 x 150,000 pixels) and is typically organized as a multi-resolution pyramid. Vanilla Transformer cannot model the hierarchical structure of WSI (such as patch cluster-level relationships), which makes it incapable of learning hierarchical WSI representation. To address these problems, in this article, we propose a novel Sparse and Hierarchical Transformer (SH-Transformer) for survival analysis. Specifically, we introduce sparse self-attention to alleviate the overfitting problem, and propose a hierarchical Transformer structure to learn the hierarchical WSI representation. Experimental results based on three WSI datasets show that the proposed framework outperforms the state-of-the-art methods.
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种英语
WOS记录号WOS:001139615300021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38397]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Fa
作者单位1.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China
2.Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
5.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou 215123, Peoples R China
6.Univ Sci & Technol China, Sch Biomed Engn, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Rui,Lv, Zhilong,Yang, Zhidong,et al. Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,28(1):7-18.
APA Yan, Rui,Lv, Zhilong,Yang, Zhidong,Lin, Senlin,Zheng, Chunhou,&Zhang, Fa.(2024).Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(1),7-18.
MLA Yan, Rui,et al."Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.1(2024):7-18.

入库方式： OAI收割

来源：计算技术研究所

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