LightAWNet: Lightweight adaptive weighting network based on dynamic convolutions for medical image segmentation
LightAWNet successfully balances efficiency and accuracy in medical image segmentation. The innovative use of spatial attention, dual-branch feature extraction, and optimized up-sampling operations contribute to its superior performance. These findings offer valuable insights for the development of resource-efficient yet highly accurate segmentation models in medical imaging. The code will be made available at https://github.com/zjmiaprojects/lightawnet upon acceptance for publication.
Authors
- Ming Xia
- Xiaoyan Wang (ORCID: https://orcid.org/0000-0003-3477-0102)
- Bangze Zhang (ORCID: https://orcid.org/0000-0002-2803-3109)
- Xiaoting Shen
- Xiaojie Huang (ORCID: https://orcid.org/0000-0001-8267-9990)
- Jianhao Yu (ORCID: https://orcid.org/0000-0002-2906-4193)
Institutions
- Stomatology Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/acm2.14584
- Citations
- 8
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.99
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province