arXiv 2404.195792024-04-30eess.IVcs.CVcs.LG

使用高阶动态模式分解和适用于小数据集的视觉Transformer对超声心动图图像进行自动心脏病理识别

Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets

Universidad Politécnica de Madrid（马德里理工大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Center for Computational Simulation (CCS)（计算仿真中心）

Andrés Bell-Navas, Nourelhouda Groun, María Villalba-Orero, Enrique Lara-Pezzi, Jesús Garicano-Mena, Soledad Le Clainche

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AI总结：

本文提出一种两阶段自动心脏病理识别系统，首次将高阶动态模式分解用于医学数据增强与特征提取，并训练适用于小数据集的视觉Transformer，在超声心动图序列上取得优于预训练CNN的效果。

摘要：

心脏病是人类死亡的主要国际原因。根据世界卫生组织的数据，每年有近1800万人因心脏病去世。考虑到医疗数据的增加，健康产业面临着开发早期且准确的心脏病识别系统的巨大压力。在这项工作中，提出了一种基于新型深度学习框架的自动心脏病理识别系统，该系统实时分析超声心动图视频序列。该系统分两个阶段工作。第一阶段将超声心动图序列数据库中的数据转换为机器学习兼容的带标注图像集合，可用于任何类型的基于机器学习的框架的训练阶段，更具体地说是用于深度学习。这包括使用高阶动态模式分解（HODMD）算法，据作者所知，这是首次在医学领域将该算法同时用于数据增强和特征提取。第二阶段专注于构建和训练一个视觉Transformer（ViT），这在相关文献中几乎未被探索过。该ViT经过调整，即使在小型数据集上也能有效地从头开始训练。所设计的神经网络分析来自超声心动图序列的图像，以预测心脏状态。获得的结果表明了所提出系统的优越性以及HODMD算法的有效性，甚至优于预训练的卷积神经网络（CNN），而后者迄今为止是文献中的首选方法。

英文摘要：

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine-learning-compatible collection of annotated images which can be used in the training stage of any kind of machine learning-based framework, and more specifically with deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors' knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the superiority of the proposed system and the efficacy of the HODMD algorithm, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

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