Classifier-Derived Guidance for Diffusion-Based Medical Dataset Distillation

Medical image analysis often relies on large annotated datasets, whose collection, storage, and sharing are costly and constrained by privacy concerns. Dataset distillation offers a potential solution by constructing compact synthetic datasets that retain task-relevant information from the original data. However, generative distillation methods may not adequately preserve class-specific information in medical images, where clinically distinct classes often exhibit similar visual characteristics. To address this issue, we propose a classifier-derived guidance framework for diffusion-based medical dataset distillation. The framework uses predictions from a pretrained noise-conditioned classifier to guide diffusion sampling through three objectives. Classifier guidance (CG) promotes class alignment, whereas entropy and margin guidance encourage boundary-oriented sampling. We analyze guidance-strength sensitivity on three medical imaging datasets across images-per-class (IPC) budgets and evaluate validation-selected COVID-19 Radiography configurations on the test subset using three downstream architectures. Classifier diagnostics distinguish prediction–conditioning agreement from downstream utility, while learning curves illustrate differences in validation behavior despite low training loss. These findings characterize the configuration-dependent utility of classifier-derived guidance for compact synthetic medical datasets.