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Grant support
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) .
Anàlisi d'autories institucional
Bell-Navas, AndresAutor (correspondència)Garicano-Mena, JesusAutor o coautorLe Clainche, SoledadAutor o coautorAutomatic Cardiac Pathology Recognition in Echocardiography Images using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Publicat a:Expert Systems With Applications. 264 125849- - 2025-03-10 264(), DOI: 10.1016/j.eswa.2024.125849
Autors: Bell-Navas, A; Groun, N; Villalba-Orero, M; Lara-Pezzi, E; Garicano-Mena, J; Le Clainche, S
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Resum
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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Impacte bibliomètric. Anàlisi de la contribució i canal de difusió
El treball ha estat publicat a la revista Expert Systems With Applications a causa de la seva progressió i el bon impacte que ha aconseguit en els últims anys, segons l'agència WoS (JCR), s'ha convertit en una referència en el seu camp. A l'any de publicació del treball, 2025, es trobava a la posició 28/204, aconseguint així situar-se com a revista Q1 (Primer Cuartil), en la categoria Computer Science, Artificial Intelligence.
Independentment de l'impacte esperat determinat pel canal de difusió, és important destacar l'impacte real observat de la pròpia aportació.
Segons les diferents agències d'indexació, el nombre de citacions acumulades per aquesta publicació fins a la data 2025-07-28:
- Scopus: 1
Impacte i visibilitat social
Anàlisi del lideratge dels autors institucionals
Hi ha un lideratge significatiu, ja que alguns dels autors pertanyents a la institució apareixen com a primer o últim signant, es pot apreciar en el detall: Primer Autor (BELL NAVAS, ANDRES) i Últim Autor (LE CLAINCHE MARTINEZ, SOLEDAD).
l'autor responsable d'establir les tasques de correspondència ha estat BELL NAVAS, ANDRES.