TY  - JOUR 
AU  - Coll, L.
AU  - Pareto, D.
AU  - Carbonell-Mirabent, P.
AU  - Cobo-Calvo, Á.
AU  - Arrambide, G.
AU  - Vidal-Jordana, Á.
AU  - Comabella, M.
AU  - Castilló, J.
AU  - Rodríguez-Acevedo, B.
AU  - Zabalza, A.
AU  - Galán, I.
AU  - Midaglia ., Luciana Soledad
AU  - Nos, C.
AU  - Salerno, A.
AU  - Auger, C.
AU  - Alberich, M.
AU  - Río, J.
AU  - Sastre-Garriga, J.
AU  - Oliver, A.
AU  - Montalban, X.
AU  - Rovira, À.
AU  - Tintoré, M.
AU  - Lladó, X.
AU  - Tur, C.
PY  - 2023
SN  - 2213-1582
UR  - http://hdl.handle.net/20.500.11940/21309
AB  - The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of...
LA  - eng
KW  - Humans
KW  - Multiple Sclerosis
KW  - Deep Learning
KW  - Magnetic Resonance Imaging
KW  - Brain
KW  - Attention
KW  - Blindness
TI  - Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI
DO  - 10.1016/j.nicl.2023.103376
T2  - NeuroImage: Clinical
KW  - AS Vigo
KW  - CHUVI
VL  - 38
ER  -