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The authors acknowledge the Grants TED2021-129774B-C21, TED2021-129774B-C22, and PLEC2022-009235, funded by the Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and by the European Union "NextGenerationEU"/PRTR: the first one to A.B-N and N.G, and the next two to E.L-P. S.L.C acknowledges the Grant PID2023-147790OB-I00 funded by MCIU/AEI/10.13039/50110001103 3/FEDER, UE. The authors also acknowledge the Grant PEJ-2019-TL/BMD-12831 from Comunidad de Madrid to E.L-P and to M.V-O, and a Juan de la Cierva Incorporacion Grant (IJCI-2016-27698) to M.V-O. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII) , the Ministerio de Ciencia, Innovacion y Universidades (MICIU) , and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (Grant CEX2020-001041-S funded by MICIU/AEI/10.13039/501100011033) .

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

Publicado en:Expert Systems With Applications. 264 125849- - 2025-03-10 264(), DOI: 10.1016/j.eswa.2024.125849

Autores: Bell-Navas, A; Groun, N; Villalba-Orero, M; Lara-Pezzi, E; Garicano-Mena, J; Le Clainche, S

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Resumen

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 phase of any kind of machine learning-based framework, including 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 efficacy of the HODMD algorithm and the superiority of the proposed system, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

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