A More Interpretable Classifier For Multiple Sclerosis

Over the past years, deep learning proved its effectiveness in medical imaging for diagnosis or segmentation. Nevertheless, to be fully integrated in clinics, these methods must both reach good performances and convince area practitioners about their interpretability. Thus, an interpretable model sh...

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Bibliographic Details
Published in2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) pp. 1062 - 1066
Main Authors Wargnier-Dauchelle, V., Grenier, T., Durand-Dubief, F., Cotton, F., Sdika, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.04.2021
Subjects
attribution maps
Brain modeling
Deep learning
Focusing
Image segmentation
Magnetic resonance
Magnetic resonance imaging
MRI
Multiple sclerosis
tissue probability maps
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Summary:Over the past years, deep learning proved its effectiveness in medical imaging for diagnosis or segmentation. Nevertheless, to be fully integrated in clinics, these methods must both reach good performances and convince area practitioners about their interpretability. Thus, an interpretable model should make its decision on clinical relevant information as a domain expert would. With this purpose, we propose a more interpretable classifier focusing on the most widespread autoimmune neuroinflammatory disease: multiple sclerosis. This disease is characterized by brain lesions visible on MRI (Magnetic Resonance Images) on which diagnosis is based. Using Integrated Gradients attributions, we show that the utilization of brain tissue probability maps instead of raw MR images as deep network input reaches a more accurate and interpretable classifier with decision highly based on lesions.
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9434074