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.

我们提出了一种新的医学图像数据集精华方法，通过设计新的渐进轨迹匹配策略提高了医学图像数据集精华的训练稳定性，并通过动态消除不同图像之间的重叠来改善合成数据集的多样性和最终性能，进而在各种模态和配置上建立了一个新的医学图像数据集精华基准，实验证明我们提出的方法相比之前的最先进方法平均提高了 8.33%，当 ipc=2 时，提高了 11.7%。