2026-04-17T06:40:02Zhttps://recercat.cat/oai/request
oai:recercat.cat:20.500.14342/60782026-03-20T01:12:44Zcom_2072_482405com_2072_183628col_2072_482415
RECERCAT author Malé, Jordi author Xirau Guardans, Victor author Fortea, Juan author Heuzé, Yann author Martínez-Abadías, Neus author Sevillano, Xavier 2026-03-20T01:12:44Z 2026-03-20T01:12:44Z 2024-09-25 9781643685434 1879-8314 https://hdl.handle.net/20.500.14342/6078 https://doi.org/10.3233/FAIA240415 Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the links between cognitive performance and brain anatomy remain unclear. The complexity of analyzing brain MRI scans requires expertise and time, prompting the exploration of artificial intelligence for automated assistance. In this context, unsupervised deep learning techniques, particularly Transformers and Autoencoders, offer a solution by learning the distribution of healthy brain anatomy and detecting alterations in unseen scans. In this work, we evaluate several unsupervised models to reconstruct healthy brain scans and detect synthetic anomalies. eng © L'autor/a Attribution-NonCommercial 4.0 International Unsupervised Deep learning Autoenders Brain MRI scans Anomaly detection Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans info:eu-repo/semantics/article