IAP-TransUNet: integration of the attention mechanism and pyramid pooling for medical image segmentation
The experimental results demonstrate that the proposed network has improved the effectiveness and shows strong performance on both CT and MRI data, which suggests its potential for generalization across different medical imaging modalities.
Authors
- Shuting Zhao (ORCID: https://orcid.org/0009-0008-1379-9954)
- Hongmeng Yu (ORCID: https://orcid.org/0000-0002-2578-9103)
- Xinrong Chen (ORCID: https://orcid.org/0009-0009-3706-2102)
- Yang Shi (ORCID: https://orcid.org/0000-0002-2220-9177)
- Fang Li (ORCID: https://orcid.org/0009-0003-2442-6604)
- Quan Liu
Institutions
- Shanghai University of Electric Power (CN)
- Fudan University (CN)
- Eye & ENT Hospital of Fudan University (CN)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.3389/fnbot.2025.1706626
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00