Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis

This paper uses convolutional neural networks to extract latent MS lesion patterns that are associated with early disease activity using lesion masks computed from baseline MR images, and demonstrates that the prediction model can achieve an accuracy rate significantly higher than the traditional MRI biomarker of lesion load.