Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images

doi:10.1109/JBHI.2023.3307584

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	Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images
	Yan, Rui 1,2,3,4; Lv, Zhilong 3; Yang, Zhidong 3; Lin, Senlin 3; Zheng, Chunhou 5; Zhang, Fa 6
	2024
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
卷号	28 期号:1 页码:7-18
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.
关键词	Hierarchical representation pathological image analysis sparse transformer survival analysis
DOI	10.1109/JBHI.2023.3307584
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号	WOS:001139615300021
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38398
专题	中国科学院计算技术研究所
通讯作者	Zhang, Fa
作者单位	1.Univ Sci & Technol China, Sch Biomed Engn, Hefei 230026, Peoples R China 2.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou 215123, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China 6.Beijing Inst Technol, Sch Med Technol, Beijing 100081, 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.