InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography
This addresses storage and computational challenges for medical practitioners and researchers in cardiology, but it is an incremental improvement as it builds on existing dataset distillation techniques.
The paper tackled the problem of efficiently storing and training models on large echocardiographic video datasets by proposing a graph-based method to distill a compact synthetic subset, achieving a test accuracy of 69.38% using only 25 synthetic videos.
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.