2026-04-17T01:16:57Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3838222025-07-23T01:18:05Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Garcia Cabrera, Carles author 2022-05-23 Cardiac MRI segmentation is a clinically interesting field that can accelerate and improve diagnostics. Targeting the capability of models towards better generalizing in unseen subsets of data that can better represent minority cohorts, greatly enhancing the lives of multiple people, cheapening the diagnostics, and making current models more resilient to unseen pathologies. In this project, our aim was to study how different architectures behave in a multiview multivendor multipathology scenario with respect to these generalization capacities and explore how postprocessing can improve the results. In addition, we also assess the computational cost that these models need to ensure that they are valid for clinical products and machines that can be reached at any clinical center. Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Neural networks (Computer science) Machine learning Imaging systems in medicine Image segmentation Neural Networks Machine Learning Medical Imaging Image Segmentation Xarxes neuronals (Informàtica) Aprenentatge automàtic Imatgeria mèdica Imatges--Segmentació Multi-View & Multi-Vendor Ventricular Segmentation