  It is essential but challenging to share medical image datasets due to
privacy issues, which prohibit building foundation models and knowledge
transfer. In this paper, we propose a novel dataset distillation method to
condense the original medical image datasets into a synthetic one that
preserves useful information for building an analysis model without accessing
the original datasets. Existing methods tackle only natural images by randomly
matching parts of the training trajectories of the model parameters trained by
the whole real datasets. However, through extensive experiments on medical
image datasets, the training process is extremely unstable and achieves
inferior distillation results. To solve these barriers, we propose to design a
novel progressive trajectory matching strategy to improve the training
stability for medical image dataset distillation. Additionally, it is observed
that improved stability prevents the synthetic dataset diversity and final
performance improvements. Therefore, we propose a dynamic overlap mitigation
module that improves the synthetic dataset diversity by dynamically eliminating
the overlap across different images and retraining parts of the synthetic
images for better convergence. Finally, we propose a new medical image dataset
distillation benchmark of various modalities and configurations to promote fair
evaluations. It is validated that our proposed method achieves 8.33%
improvement over previous state-of-the-art methods on average, and 11.7%
improvement when ipc=2 (i.e., image per class is 2). Codes and benchmarks will
be released.
