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Abstract #2811

Deep Multiple Sclerosis Lesion Segmentation with Anatomical Convolution and Lesion-wise Loss

Hang Zhang1, Jinwei Zhang1, Pascal Spincemaille1, Thanh D. Nguyen1, and Yi Wang1
1Cornell University, New York, NY, United States

We propose an anatomical convolutional module to couple anatomical information into deep neural network. We further develop a loss function based on the mass center of individual lesions called lesion-wise loss, which can regularize the network training, thereby improving the performance of lesion localization and segmentation. We validate our methods on a public dataset, ISBI-15 Multiple Sclerosis Lesion Segmentation Challenge [1], where the results showed that we achieved the best performance on all published methods.

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