Dual‐branch Transformer for semi‐supervised medical image segmentation
The results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi-supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication.
Authors
- Yating Zhu (ORCID: https://orcid.org/0000-0002-0158-4859)
- Minghan Shao
- Xiaoyan Wang (ORCID: https://orcid.org/0000-0002-5487-0584)
- Xiaoting Shen
- Xiaojie Huang (ORCID: https://orcid.org/0000-0001-8267-9990)
- Ming Xia (ORCID: https://orcid.org/0000-0002-8094-2203)
- Pingli Wang (ORCID: https://orcid.org/0000-0002-1472-1242)
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
- 2024-08-12
- DOI
- https://doi.org/10.1002/acm2.14483
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.39