  Any novel medical imaging modality that differs from previous protocols e.g.
in the number of imaging channels, introduces a new domain that is
heterogeneous from previous ones. This common medical imaging scenario is
rarely considered in the domain adaptation literature, which handles shifts
across domains of the same dimensionality. In our work we rely on stochastic
generative modeling to translate across two heterogeneous domains at pixel
space and introduce two new loss functions that promote semantic consistency.
Firstly, we introduce a semantic cycle-consistency loss in the source domain to
ensure that the translation preserves the semantics. Secondly, we introduce a
pseudo-labelling loss, where we translate target data to source, label them by
a source-domain network, and use the generated pseudo-labels to supervise the
target-domain network. Our results show that this allows us to extract
systematically better representations for the target domain. In particular, we
address the challenge of enhancing performance on VERDICT-MRI, an advanced
diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When
compared to several unsupervised domain adaptation approaches, our approach
yields substantial improvements, that consistently carry over to the
semi-supervised and supervised learning settings.
