A 3D deep convolutional neural network (dCNN) was trained to differentiate MS from non-MS lesions based on the orientation and location of a central vein ('central vein sign') relative to the lesion. Excellent performance was achieved using simulated FLAIR and T2*-weighted imaging, with realistic noise levels. The dCNN may be capable of identifying other discriminatory features from multimodal human imaging data.
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