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Article

An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.

PeerJ - 1 Jan 2024

Li Yi, Xiong Xiaomin, Liu Xiaohua, Wu Yihan, Li Xiaoju, Liu Bo, Lin Bo, Li Yu, Xu Bo

Abstract excerpt

Background: Determining the status of breast cancer susceptibility genes (BRCA) is crucial for guiding breast cancer treatment. Nevertheless, the need for BRCA genetic testing among breast cancer patients remains unmet due to high costs and limited resources. This study aimed to develop a Bi-directional Self-Attention Multiple Instance Learning (BiAMIL) algorithm to detect BRCA status from hematoxylin and eosin...

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Identifiers

PMID: 39484212
DOI: 10.7717/peerj.18098
PMCID: PMC11526788

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