Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Chaojie Zhang; Zong Fan; Zhimin Wang; Lulu Sun; Yao Hao; Zhongwei Zhang; Wade Thorstad; Hiram Gay; Xiaowei Wang; Mark A. Anastasio; Hua Li

doi:10.1117/12.3007045

2 April 2024 Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Chaojie Zhang, Zong Fan, Zhimin Wang, Lulu Sun, Yao Hao, Zhongwei Zhang, Wade Thorstad, Hiram Gay, Xiaowei Wang, Mark A. Anastasio, Hua Li

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292633 (2024) https://doi.org/10.1117/12.3007045
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Histopathology whole-slide image (WSI) captures detailed structural and morphological features of tumor tissue, offering rich histological and molecular information to support clinical practice. With the development of artificial intelligence, deep learning (DL) methods have emerged to assist in automatically analyzing histopathology WSIs. It alleviates the need for tedious, time-consuming, and error-prone inspections by clinicians. Up to now, employing DL models for histopathology WSI analysis is still challenging due to the intrinsic complexity of histology characteristics of tumor tissue, high image resolution, and large image size. In this study, we proposed a transformer-based classifier with feature aggregation for cancer subtype classification using histopathology WSIs while addressing these challenges. Our method shows three advantages to improve classification performance. First, an aggregate transformer decoder is employed to learn both global and local features from WSIs. Second, the transformer architecture facilitates the decoder to learn spatial correlations among different regions in a WSI. Third, the self-attention mechanism of the transformer facilitates the generation of saliency maps to highlight regions of interest in WSIs. We evaluated our model on three cancer subtype classification tasks and demonstrated its effectiveness and performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chaojie Zhang, Zong Fan, Zhimin Wang, Lulu Sun, Yao Hao, Zhongwei Zhang, Wade Thorstad, Hiram Gay, Xiaowei Wang, Mark A. Anastasio, and Hua Li "Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292633 (2 April 2024); https://doi.org/10.1117/12.3007045

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available