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

TL;DR

This paper introduces a novel deep learning system combining Higher Order Dynamic Mode Decomposition and a Vision Transformer to accurately recognize cardiac pathologies from echocardiography videos, especially effective with small datasets.

Contribution

The study pioneers the use of HODMD for data augmentation and feature extraction in medical imaging and applies Vision Transformer architecture for cardiac pathology recognition from limited data.

Findings

HODMD improves data augmentation and feature extraction.

The Vision Transformer outperforms pretrained CNNs.

The system achieves high accuracy in real-time cardiac pathology detection.

Abstract

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.