Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation
Data heterogeneity hinders clinical deployment of medical image analysis models, and generative data augmentation helps mitigate this issue. However, recent diffusion-based methods that synthesize image-mask pairs often ignore distribution shifts between generated and real images across scenarios, and such mismatches can markedly degrade downstream performance. To address this issue, we propose AlignFlow, a flow matching model that aligns with the target reference image distribution via differentiable reward fine-tuning, and remains effective even when only a small number of reference images are provided. Specifically, we divide the training of the flow matching model into two stages: in ...
| Authors | Jie Yang, Ziqi Ye, Aihua Ke et al. |
|---|---|
| Primary category | eess.IV |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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