EMCAH-Net: an effective multi-scale context aggregation hybrid network for medical image segmentation
EMCAH-Net has demonstrated significant advantages in segmenting multi-scale, small, and boundary-blurred features in medical images. Extensive experiments on abdominal multi-organ, cardiac, and retinal vessel medical segmentation tasks confirm that EMCAH-Net surpasses previous methods, including pure CNN, pure Transformer, and hybrid architectures.
Authors
- Yanhui Guo (ORCID: https://orcid.org/0000-0002-9908-3795)
- Yu Qian (ORCID: https://orcid.org/0000-0001-8543-2774)
- Jin Yu (ORCID: https://orcid.org/0000-0003-1074-2465)
- Guoqing Chao (ORCID: https://orcid.org/0000-0002-2410-650X)
- Qiang Cai (ORCID: https://orcid.org/0000-0001-7012-9887)
- Rui Tian (ORCID: https://orcid.org/0000-0002-4415-2917)
- Danqing Liu
Institutions
- California State University, Fullerton (US)
- Harbin Institute of Technology (CN)
- Qinghai Normal University (CN)
- Chengdu University of Technology (CN)
- Shandong Women’s University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21037/qims-24-1983
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84