Diffusion Model-based Medical Image Generation as a Potential Data Augmentation Strategy for AI Applications
This study demonstrated that diffusion model-based medical image synthesis is potentially applicable as an augmentation strategy for AI, particularly in situations where access to real clinical data is limited. Optimal training parameters were also proposed by evaluating the dimensionality of feature vectors in FID calculations and the complexity of loss functions.
Authors
- Zijian Cao (ORCID: https://orcid.org/0009-0007-5584-5624)
- Yibao Zhang (ORCID: https://orcid.org/0000-0003-3658-0687)
- Jueye Zhang
- Tian Li
- Lin Chen (ORCID: https://orcid.org/0000-0002-5456-9345)
- Hao Wu (ORCID: https://orcid.org/0009-0001-6226-0452)
Institutions
- Hong Kong Polytechnic University (HK)
- Peking University (CN)
- Peking University Cancer Hospital (CN)
- Ministry of Education (ET)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2025-09-04
- DOI
- https://doi.org/10.2174/0115734056401610250827114351
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00