  This work presents a comparative study of existing and new techniques to
detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are
based on deep learning and we explore the comparative performances of transfer
learning and a deep residual network trained from scratch. We also exploit some
characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using
a fixed number of slices or 2D images from each of the axial, coronal and
sagittal planes as well as combining the three planes into one multi-plane
network. Overall we achieved a performance of 93.4% AUC on the validation data
by using the more recent deep learning architectures and data augmentation
strategies. More flexible architectures are also proposed that might help with
the development and training of models that process MRIs. We found that
transfer learning and a carefully tuned data augmentation strategy were the
crucial factors in determining best performance.
