Multi‐scale nested graph transformer with graph operations: Advancing high‐resolution chest x‐ray classification
MNGT provides an efficient and robust solution for high-resolution CXR classification, combining local detail preservation, global context modeling, and inductive bias to excel in accuracy and generalization. The framework addresses the computational bottleneck of high-resolution medical imaging and offers a viable pathway for clinical deployment in computer-aided diagnosis.
Authors
- Dongjing Shan (ORCID: https://orcid.org/0000-0001-6386-6096)
- Biao Qu (ORCID: https://orcid.org/0000-0001-5354-8043)
- Myunghee Yang
- Lu Huang (ORCID: https://orcid.org/0000-0002-1588-8669)
- Dawa Panduo
Institutions
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Tibetan Traditional Medical College (CN)
- Chongqing Medical University (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1002/mp.70003
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00