  In this work we propose HAMLET, a novel tract learning algorithm, which,
after training, maps raw diffusion weighted MRI directly onto an image which
simultaneously indicates tract direction and tract presence. The automatic
learning of fiber tracts based on diffusion MRI data is a rather new idea,
which tries to overcome limitations of atlas-based techniques. HAMLET takes a
such an approach. Unlike the current trend in machine learning, HAMLET has only
a small number of free parameters HAMLET is based on spherical tensor algebra
which allows a translation and rotation covariant treatment of the problem.
HAMLET is based on a repeated application of convolutions and non-linearities,
which all respect the rotation covariance. The intrinsic treatment of such
basic image transformations in HAMLET allows the training and generalization of
the algorithm without any additional data augmentation. We demonstrate the
performance of our approach for twelve prominent bundles, and show that the
obtained tract estimates are robust and reliable. It is also shown that the
learned models are portable from one sequence to another.
