  Multimodal imaging and correlative analysis typically require image
alignment. Contrastive learning can generate representations of multimodal
images, reducing the challenging task of multimodal image registration to a
monomodal one. Previously, additional supervision on intermediate layers in
contrastive learning has improved biomedical image classification. We evaluate
if a similar approach improves representations learned for registration to
boost registration performance. We explore three approaches to add contrastive
supervision to the latent features of the bottleneck layer in the U-Nets
encoding the multimodal images and evaluate three different critic functions.
Our results show that representations learned without additional supervision on
latent features perform best in the downstream task of registration on two
public biomedical datasets. We investigate the performance drop by exploiting
recent insights in contrastive learning in classification and self-supervised
learning. We visualize the spatial relations of the learned representations by
means of multidimensional scaling, and show that additional supervision on the
bottleneck layer can lead to partial dimensional collapse of the intermediate
embedding space.
