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GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

IEEE transactions on neural networks and learning systems - 1 Jun 2024

Zhao Wei, Chen Weidao, Li Ge, Lei Du, Yang Jiancheng, Chen Yanjing, Jiang Yingjia, Wu Jiangfen, Ni Bingbing, Sun Yeqi, Wang Shaokang, Sun Yingli, Li Ming, Liu Jun

Abstract excerpt

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help...

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PMID: 35862326
DOI: 10.1109/TNNLS.2022.3190671

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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