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.

翻译：超声心动图作为一种非侵入性实时评估心脏结构与功能的技术，在心血管疾病的诊断与监测中发挥着关键作用。然而，超声心动图视频数据规模的不断增长，在存储、计算和模型训练效率方面带来了显著挑战。数据集蒸馏通过合成一个紧凑且信息丰富的子集来保留原始数据集的关键临床特征，为此提供了一种有前景的解决方案。本研究提出了一种新颖的方法，用于蒸馏紧凑的合成超声心动图视频数据集。我们的方法利用运动特征提取捕捉时序动态，随后通过基于类别的图构建，并采用Infomap算法进行代表性样本选择。这使得我们能够选取多样且信息丰富的合成视频子集，同时保留原始数据集的基本特征。我们在EchoNet-Dynamic数据集上评估了该方法，仅使用25个合成视频即实现了69.38%的测试准确率。这些结果证明了我们的方法在医学视频数据集蒸馏中的有效性和可扩展性。