Generalization of Cardiomyopathy Classification Models Based on Feature Descriptors from Magnetic Resonance Imaging - Costa, Stephani S. H.; Gonçalves, Vagner Mendonça; Ribeiro, Matheus A. O.; Nunes, Fátima L. S. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2025)
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 generalization ability of these models. 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.