Multi‐scale contextual learning for medical image segmentation via dual distillation
Comprehensive comparisons in terms of segmentation accuracy and computational complexity unequivocally confirm that our method effectively and efficiently integrates the advantages of both CNNs and transformers, showing its suitability and significance for clinical applications.
Authors
- Lanqing Liu (ORCID: https://orcid.org/0009-0005-7522-8821)
- Xiaowei Hu (ORCID: https://orcid.org/0000-0002-5708-7018)
- Ge Ren (ORCID: https://orcid.org/0000-0003-2049-2682)
- Jing Qin (ORCID: https://orcid.org/0000-0002-7059-0929)
- Ruize Cui (ORCID: https://orcid.org/0000-0002-8371-269X)
- Youyi Song (ORCID: https://orcid.org/0000-0001-7723-3126)
Institutions
- Hong Kong Polytechnic University (HK)
- China Pharmaceutical University (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1002/mp.17506
- Citations
- 3
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.75
Funders
- Science, Technology and Innovation Commission of Shenzhen Municipality