MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images

MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images

IntroductionPulmonary granulomatous nodules (PGN) often exhibit similar CT morphological features to solid lung adenocarcinomas (SLA), making preoperative differentiation challenging. This study aims to address this diagnostic challenge by developing a novel deep learning model.MethodsThis study pro...

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Bibliographic Details
Main Authors: Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Medicine
Subjects:
lung cancer
pulmonary granulomatous nodule
solid lung adenocarcinomas
CT image
self-supervised learning
masked autoencoder
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1507258/full
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https://www.frontiersin.org/articles/10.3389/fmed.2025.1507258/full

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