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Dropout is conventionally used during the training phase as regularizationmethod and for quantifying uncertainty in deep learning. We propose to usedropout during training as well as inference steps, and average multiplepredictions to improve the accuracy, while reducing and quantifying theuncertainty. The results are evaluated for fractional anisotropy (FA) and meandiffusivity (MD) maps which are obtained from only 3 direction scans. With ourmethod, accuracy can be improved significantly compared to network outputswithout dropout, especially when the training dataset is small. Moreover,confidence maps are generated which may aid in diagnosis of unseen pathology orartifacts.

Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout