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.

提出了一种基于深度学习框架的自动心脏病理识别系统，该系统通过实时心脏超声图像序列进行分析，包括使用 HODMD 算法进行数据增强和特征提取，并在第二阶段使用 Vision Transformer（ViT）进行有效的从零开始训练，结果表明该系统的优越性和 HODMD 算法的有效性。

利用高阶动态模态分解和视觉 Transformer 对小型数据集中的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