  Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and
management of cardiovascular disease. Deep learning methods have proven to
deliver segmentation results comparable to human experts in CMR imaging, but
there have been no convincing results for the problem of end-to-end
segmentation and diagnosis from CMR. This is in part due to a lack of
sufficiently large datasets required to train robust diagnosis models. In this
paper, we propose a learning method to train diagnosis models, where our
approach is designed to work with relatively small datasets. In particular, the
optimisation loss is based on multi-task learning that jointly trains for the
tasks of segmentation and diagnosis classification. We hypothesize that
segmentation has a regularizing effect on the learning of features relevant for
diagnosis. Using the 100 training and 50 testing samples available from the
Automated Cardiac Diagnosis Challenge (ACDC) dataset, which has a balanced
distribution of 5 cardiac diagnoses, we observe a reduction of the
classification error from 32% to 22%, and a faster convergence compared to a
baseline without segmentation. To the best of our knowledge, this is the best
diagnosis results from CMR using an end-to-end diagnosis and segmentation
learning method.
