A UTLO-Based Approach for Small-Sample Imbalanced Medical Image Generation

Medical image datasets often suffer from limited training samples and severe class imbalance. This scarcity hinders deep learning models from learning representative features and leads to degraded performance on minority classes. In this work, we adapt the Unconditional Training at Lower Resolutions (UTLO) framework to long-tailed brain MRI data and conduct systematic evaluations on a brain tumor dataset. UTLO enables knowledge sharing between frequent and rare classes by training the generator unconditionally at lower resolutions to capture class-agnostic structures, while employing conditional generation at higher resolutions to refine class-specific details. Meanwhile, the discriminator is optimized with a multi-objective loss across low- and high-resolution stages to improve robustness and alleviate mode collapse under limited data. We evaluate the generated images using Frechet Inception Distance (FID), Kernel Inception Distance (KID), Inception Score (IS), and their few-shot variants, i.e., FID-FS (FID on few-shot/rare classes) and KID-FS (KID on few-shot/rare classes). Qualitative visualization is also provided to assess perceptual realism and diversity. Experimental results demonstrate that UTLO achieves stable generation performance across categories and yields noticeable improvements on rare classes.