MSA <sup>2</sup> -Net: Utilizing self-adaptive convolution module to extract multi-scale information in medical image segmentation
-Net achieves competitive Dice scores of 86.49%, 92.56%, 93.37%, and 92.98% on the Synapse, ACDC, Kvasir, and ISIC2017 datasets, respectively. Extensive experiments validate the model's robustness in handling complex spatial variations across diverse medical modalities.
Authors
- ZhengYou Qin
- Xiaosen Li (ORCID: https://orcid.org/0009-0001-7364-630X)
- Yuanxu Gong
- Xiao Qin
Institutions
- Sichuan University (CN)
- Nanning Normal University (CN)
Publication Details
- Journal
- Science Progress
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1177/00368504261432413
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and Technology of the People's Republic of China