Machine learning strongly enhances diffusion MRI in terms of acquisition speed and quality of results. Different machine learning tasks are applicable in different situations: labels for training might be available only for healthy data or only for common but not rare diseases; training labels might be available voxel-wise, or only scan-wise. This leads to various tasks beyond supervised learning. Here we examine whether it is possible to perform accurate voxel-wise MS lesion detection if only scan-wise training labels are used. We use negative-unlabeled learning (an equivalent of positive-unlabeled learning) and achieve an AUC of 0.77.