Towards Medical Image Dataset Distillation: A Progressive Trajectory Matching and Dynamic Diversity Enhancing Method

Sharing medical image sets is essential yet challenging due to limited resource and strict regulatory, which significantly hinder the development of medical foundation models and the transfer of medical domain knowledge. In this paper, we utilize medical image dataset distillation techniques to enable effective model construction and knowledge transfer without requiring downstream users to directly access the original data. Trajectory matching has become the mainstream distillation technique, yet our empirical findings on medical image datasets reveal two critical phenomena: instability of distillation optimization and redundancy of synthetic images. To investigate these phenomena, we conduct a theoretical analysis from the perspectives of bi-level optimization and neural tangent kernel, and formally define them as two core challenges: trajectory matching drift and intra-class diversity decay, which underscore the need for more stable and diversity-preserving trajectory matching methods. Guided by the analysis results, we propose a novel progressive and diverse dataset distillation (PD$^{3}$) method, which incorporates (1) a progressive trajectory matching paradigm that progressively unrolls the student training trajectory to extend the matching horizon, while adaptively selecting optimal expert matching trajectory to ensure stable and convergent distillation optimization; and (2) a dynamic diversity enhancing module that alleviates the redundancy of synthetic images by enhancing intra-class distribution and breaking redundancy accumulation. To facilitate fair evaluation, we establish a comprehensive medical image dataset distillation benchmark covering diverse medical image modalities. Extensive experiments show that the proposed method achieves an average absolute improvement of 10.20% and a relative gain of 20.09% over prior state-of-the-art methods, highlighting its effectiveness and practical potential.