CMMIQA: a prompt-driven cross-modality multi-organ medical image quality assessment model
Experimental results show that this model is superior to existing no-reference image quality assessment (IQA) methods and medical IQA models, while being able to transfer to other 2D and 3D medical image datasets as a foundation.
Authors
- Yue Sun (ORCID: https://orcid.org/0000-0001-6116-7435)
- Mingxiang Wu (ORCID: https://orcid.org/0000-0002-5297-438X)
- Siyi Xun (ORCID: https://orcid.org/0000-0002-9392-5445)
- Mingwei Wang (ORCID: https://orcid.org/0000-0001-7071-9166)
- Tao Tan (ORCID: https://orcid.org/0000-0002-5573-1105)
- Yapeng Wang (ORCID: https://orcid.org/0000-0001-6317-9181)
- Xiaohong Liu
Institutions
- Shanghai Jiao Tong University (CN)
- Affiliated Hospital of Hangzhou Normal University (CN)
- ShenZhen People’s Hospital (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-07-01
- DOI
- https://doi.org/10.21037/qims-2025-127
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00