用图模型从超多心动超声视频中提炼出25个代表性样本
InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography
- 基于运动特征构建视频间关系图，用算法选出最具代表性的样本
- 仅用25个合成视频就达到69.38%准确率，接近完整数据集效果
- 适合医疗影像数据压缩与高效训练，尤其适用于超声视频场景
心动超声在心血管疾病诊断与监测中具有关键作用，能实时评估心脏结构与功能。然而，日益增长的超声视频数据量带来了存储、计算和模型训练效率的挑战。数据蒸馏通过生成紧凑且信息丰富的数据子集，为解决该问题提供了新思路。本文提出一种新型方法，用于提炼紧凑的合成心动超声视频数据集。该方法首先提取运动特征以捕捉时序动态，再按类别构建图结构，并利用Infomap算法选择代表性样本。由此选出的合成视频子集兼具多样性与信息量，有效保留原始数据的关键临床特征。我们在EchoNet-Dynamic数据集上进行评估，仅使用25个合成视频即实现69.38%的测试准确率，验证了该方法的有效性与可扩展性。
原文摘要 · Abstract (English)
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.
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