  Magnetic Resonance Imaging (MRI) of the brain can come in the form of
different modalities such as T1-weighted and Fluid Attenuated Inversion
Recovery (FLAIR) which has been used to investigate a wide range of
neurological disorders. Current state-of-the-art models for brain tissue
segmentation and disease classification require multiple modalities for
training and inference. However, the acquisition of all of these modalities are
expensive, time-consuming, inconvenient and the required modalities are often
not available. As a result, these datasets contain large amounts of
\emph{unpaired} data, where examples in the dataset do not contain all
modalities. On the other hand, there is smaller fraction of examples that
contain all modalities (\emph{paired} data) and furthermore each modality is
high dimensional when compared to number of datapoints. In this work, we
develop a method to address these issues with semi-supervised learning in
translating between two neuroimaging modalities. Our proposed model,
Semi-Supervised Adversarial CycleGAN (SSA-CGAN), uses an adversarial loss to
learn from \emph{unpaired} data points, cycle loss to enforce consistent
reconstructions of the mappings and another adversarial loss to take advantage
of \emph{paired} data points. Our experiments demonstrate that our proposed
framework produces an improvement in reconstruction error and reduced variance
for the pairwise translation of multiple modalities and is more robust to
thermal noise when compared to existing methods.
