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Vision Transformers for Computational Histopathology

Authors
 Hongming Xu  ;  Qi Xu  ;  Fengyu Cong  ;  Jeonghyun Kang  ;  Chu Han  ;  Zaiyi Liu  ;  Anant Madabhushi  ;  Cheng Lu 
Citation
 IEEE REVIEWS IN BIOMEDICAL ENGINEERING, Vol.17 : 63-79, 2024-01 
Journal Title
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
ISSN
 1937-3333 
Issue Date
2024-01
MeSH
Electric Power Supplies* ; Humans ; Image Processing, Computer-Assisted* ; Technology
Abstract
Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.
Full Text
https://ieeexplore.ieee.org/document/10190115
DOI
10.1109/rbme.2023.3297604
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Jeonghyun(강정현) ORCID logo https://orcid.org/0000-0001-7311-6053
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/198549
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