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Abstract #3524

Class activation mapping methods for interpreting deep learning models in the classification of MRI with subtypes of multiple sclerosis

Jinseo Lee1, Daniel McClement2, Glen Pridham1, Olayinka Oladosu1, and Yunyan Zhang1
1University of Calgary, Calgary, AB, Canada, 2University of British Columbia, Vancouver, BC, Canada

As deep learning technologies continue to advance, the availability of reliable methods to accurately interpret these models is critical. Based on a trained deep learning model (VGG19) for image classification, we have shown that methods using class activation mapping (CAM) and Grad-CAM have the potential to detect the most critical MRI feature patterns associated with relapsing remitting and secondary progressive multiple sclerosis, and healthy controls, and that these patterns seem to differentiate the two continuing subtypes of MS. This can help further understand the mechanisms of disease development and discover new biomarkers for clinical use.

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