Automatic new lesion detection

Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach

Frontiers in Neuroscience, 2022. Quality index: (JCR N IF 3.707, Q2(97/272)).

Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images

PhD thesis in Brain Medical Image Analysis, Department of Computer Architecture and Technology, University of Girona, Spain, 2020.

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

NeuroImage: Clinical, 2020. Quality index: (JCR N IF 3.943, Q1(3/14)).

Detecting the appearance of new T2-w multiple sclerosis lesions in longitudinal studies using deep convolutional neural networks

Abstract in Multiple Sclerosis Journal, Stockholm Sweden, September, 2019 (JCR CN IF:5.649 Q1(23/199)).

A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

NeuroImage: Clinical, 2018. Quality index: (JCR N IF 3.943, Q1(3/14)).

Supervised detection of newly appearing T2-w multiple sclerosis lesions with subtraction and deformation fields features

Abstract in Multiple Sclerosis Journal, Paris, France, October, 2017 (JCR CN IF:5.649 Q1(23/199)).