  Calcified plaque in the aorta and pelvic arteries is associated with coronary
artery calcification and is a strong predictor of heart attack. Current
calcified plaque detection models show poor generalizability to different
domains (ie. pre-contrast vs. post-contrast CT scans). Many recent works have
shown how cross domain object detection can be improved using an image
translation model which translates between domains using a single shared latent
space. However, while current image translation models do a good job preserving
global/intermediate level structures they often have trouble preserving tiny
structures. In medical imaging applications, preserving small structures is
important since these structures can carry information which is highly relevant
for disease diagnosis. Recent works on image reconstruction show that complex
real-world images are better reconstructed using a union of subspaces approach.
Since small image patches are used to train the image translation model, it
makes sense to enforce that each patch be represented by a linear combination
of subspaces which may correspond to the different parts of the body present in
that patch. Motivated by this, we propose an image translation network using a
shared union of subspaces constraint and show our approach preserves subtle
structures (plaques) better than the conventional method. We further applied
our method to a cross domain plaque detection task and show significant
improvement compared to the state-of-the art method.
