InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography

• URL: http://arxiv.org/abs/2512.09422v1
• Date: Wed, 10 Dec 2025 08:39:25 GMT
• Title: InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography
• Authors: Zhe Li, Hadrien Reynaud, Alberto Gomez, Bernhard Kainz,
• Abstract summary: We propose a novel approach for distilling a compact synthetic echocardiographic video dataset.<n>We evaluate our approach on the EchoNet-Dynamic datasets and achieve a test accuracy of (69.38%) using only (25) synthetic videos.
• Score: 12.676788334083332
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Echocardiography playing 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.

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