Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis

Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis

The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks ai...

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Bibliographic Details
Main Authors: Alina Lopatina, Stefan Ropele, Renat Sibgatulin, Jürgen R. Reichenbach, Daniel Güllmar
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-12-01
Series:Frontiers in Neuroscience
Subjects:
convolutional neural network
deep learning
explainability
magnetic resonance imaging
multiple sclerosis
susceptibility-weighted imaging
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2020.609468/full

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https://www.frontiersin.org/articles/10.3389/fnins.2020.609468/full

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