中文版 | English
Title

PathTR: Context-Aware Memory Transformer for Tumor Localization in Gigapixel Pathology Images

Author
Corresponding AuthorLuo, Lin
DOI
Publication Years
2023
Conference Name
16th Asian Conference on Computer Vision, ACCV 2022
ISSN
0302-9743
EISSN
1611-3349
ISBN
9783031263507
Source Title
Volume
13846 LNCS
Pages
115-131
Conference Date
December 4, 2022 - December 8, 2022
Conference Place
Macao, China
Publisher
Abstract
With the development of deep learning and computational pathology, whole-slide images (WSIs) are widely used in clinical diagnosis. A WSI, which refers to the scanning of conventional glass slides into digital slide images, usually contains gigabytes of pixels. Most existing methods in computer vision process WSIs as many individual patches, where the model infers the patches one by one to synthesize the final results, neglecting the intrinsic WSI-wise global correlations among the patches. In this paper, we propose the PATHology TRansformer (PathTR), which utilizes the global information of WSI combined with the local ones. In PathTR, the local context is first aggregated by a self-attention mechanism, and then we design a recursive mechanism to encode the global context as additional states to build the end to end model. Experiments on detecting lymph-node tumor metastases for breast cancer show that the proposed PathTR achieves the Free-response Receiver Operating Characteristic Curves (FROC) score of 87.68%, which outperforms the baseline and NCRF method with +8.99% and +7.08%, respectively. Our method also achieves a significant 94.25% sensitivity at 8 false positives per image.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
SUSTech Authorship
Corresponding
Language
English
Indexed By
Funding Project
Acknowledgement. This research was supported in part by the Foundation of Shen-zhen Science and Technology Innovation Committee (JCYJ20180507181527806). We also thank Qiuchuan Liang (Beijing Haidian Kaiwen Academy, Beijing, China) for preprocessing data.This research was supported in part by the Foundation of Shenzhen Science and Technology Innovation Committee (JCYJ20180507181527806). We also thank Qiuchuan Liang (Beijing Haidian Kaiwen Academy, Beijing, China) for preprocessing data.
EI Accession Number
20231113734213
EI Keywords
Deep learning ; Diagnosis ; Tumors
ESI Classification Code
Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6
Data Source
EV Compendex
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/531330
DepartmentSouthern University of Science and Technology
Affiliation
1.Peking University, Beijing, China
2.Beijing Institute of Collaborative Innovation, Beijing, China
3.Southern University of Science and Technology, Shenzhen, China
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Qin, Wenkang,Xu, Rui,Jiang, Shan,et al. PathTR: Context-Aware Memory Transformer for Tumor Localization in Gigapixel Pathology Images[C]:Springer Science and Business Media Deutschland GmbH,2023:115-131.
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