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.