  Dropout is conventionally used during the training phase as regularization
method and for quantifying uncertainty in deep learning. We propose to use
dropout during training as well as inference steps, and average multiple
predictions to improve the accuracy, while reducing and quantifying the
uncertainty. The results are evaluated for fractional anisotropy (FA) and mean
diffusivity (MD) maps which are obtained from only 3 direction scans. With our
method, accuracy can be improved significantly compared to network outputs
without dropout, especially when the training dataset is small. Moreover,
confidence maps are generated which may aid in diagnosis of unseen pathology or
artifacts.
