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

TL;DR

This paper introduces a graph-based method for distilling large echocardiographic video datasets into a small, synthetic, yet informative subset that preserves key clinical features, improving efficiency in medical data handling.

Contribution

We propose a novel graph-based dataset distillation technique leveraging motion features and the Infomap algorithm for synthetic echocardiography video selection.

Findings

Achieved 69.38% accuracy with only 25 synthetic videos

Effectively preserves essential dataset characteristics

Demonstrates scalability for large medical video datasets

Abstract

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.