Investigating Cardiac Magnetic Resonance Imaging Feature Descriptors for Generalization of Cardiomyopathy Classification Models

  • Stephani S. H. Costa USP
  • Vagner Mendonça Gonçalves USP / IFSP
  • Matheus A. O. Ribeiro USP
  • Fátima L. S. Nunes USP

Resumo


Supervised Machine Learning (SML) models can help physicians to compose more accurate diagnoses. Due to the diversity of machines, systems, and protocols, exams conducted at different centers may have high variability, decreasing the model generalization ability. This paper aims to evaluate generalization ability of SML models for cardiomyopathy classification in Cardiac Magnetic Resonance Imaging (CMRI), using a set of left ventricle morphological and motion features. We performed cross-validation tests on two public CMRI databases, comparing intraand inter-dataset performances. The results are promising and demonstrate that the implemented features can contribute to building generalizable classification models.

Referências

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Publicado
09/06/2025
COSTA, Stephani S. H.; GONÇALVES, Vagner Mendonça; RIBEIRO, Matheus A. O.; NUNES, Fátima L. S.. Investigating Cardiac Magnetic Resonance Imaging Feature Descriptors for Generalization of Cardiomyopathy Classification Models. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 67-72. ISSN 2763-8987. DOI: https://doi.org/10.5753/sbcas_estendido.2025.7909.