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Abstract #3480

Negative-Unlabeled Learning for Diffusion MRI

Phillip Swazinna1, Vladimir Golkov1, Ilona Lipp2, Eleonora Sgarlata2,3, Valentina Tomassini2,4, Derek K. Jones2, and Daniel Cremers1

1Department of Informatics, Technical University of Munich, Garching, Germany, 2CUBRIC, Cardiff University, Cardiff, United Kingdom, 3Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy, 4Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom

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.

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