Echocardiography plays a critical role in the diagnosis and monitoring of cardiovascular diseases as a non-invasive real-time assessment of cardiac structure and function. However, the growing scale of echocardiographic video data presents significant challenges in terms of storage, computation, and model training efficiency. Dataset distillation offers a promising solution by synthesizing a compact, informative subset of data that retains the key clinical features of the original dataset. In this work, we propose a novel approach for distilling a compact synthetic echocardiographic video dataset. Our method leverages motion feature extraction to capture temporal dynamics, followed by class-wise graph construction and representative sample selection using the Infomap algorithm. This enables us to select a diverse and informative subset of synthetic videos that preserves the essential characteristics of the original dataset. We evaluate our approach on the EchoNet-Dynamic datasets and achieve a test accuracy of 69.38% using only 25 synthetic videos. These results demonstrate the effectiveness and scalability of our method for medical video dataset distillation.
@article{arxiv.2512.09422,
title = {InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography},
author = {Zhe Li and Hadrien Reynaud and Alberto Gomez and Bernhard Kainz},
journal= {arXiv preprint arXiv:2512.09422},
year = {2025}
}