Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model
The Swin Transformer model can predict the pathological subtype of pure ground-glass nodules. Compared with the performance of radiologists, the deep learning model performs better. Swin Transformer model can be used as a tool for preoperative diagnosis.
Authors
- Yanhua Wen (ORCID: https://orcid.org/0000-0001-8677-5680)
- Menna Allah Mahmoud (ORCID: https://orcid.org/0000-0003-4329-2405)
- Wensheng Wu (ORCID: https://orcid.org/0000-0002-0436-7112)
- Huicong Chen
- Yubao Guan
- Xiaohuan Pan
- Yingying Zhang (ORCID: https://orcid.org/0009-0006-4533-4292)
Institutions
- Chinese General Hospital College of Nursing and Liberal Arts (PH)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1186/s13244-025-02113-3
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Guangzhou Medical University
- Innovative Research Group Project of the National Natural Science Foundation of China
- National Natural Science Foundation of China