MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images
The proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.
Authors
- Shouliang Qi (ORCID: https://orcid.org/0000-0003-0977-1939)
- Yudong Yao (ORCID: https://orcid.org/0000-0003-3868-0593)
- Y. H. Guan (ORCID: https://orcid.org/0009-0003-9156-7538)
- Yong Yue
- Hang Sun (ORCID: https://orcid.org/0000-0002-4021-5722)
- Yanhua Wen
- Jingxu Li (ORCID: https://orcid.org/0009-0006-1707-4091)
- Wei Qian (ORCID: https://orcid.org/0009-0005-5417-5310)
- Tianhu Zhao
Institutions
- Stevens Institute of Technology (US)
- Shenyang Ligong University (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- China Medical University (CN)
- Guangzhou Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-02-12
- DOI
- https://doi.org/10.3389/fmed.2025.1507258
- Citations
- 5
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 7.59
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities