Deformation equivariant cross-modality image synthesis with paired non-aligned training data. (arXiv:2208.12491v1 [cs.CV])

Cross-modality image synthesis is an active research topic with multiple
medical clinically relevant applications. Recently, methods allowing training
with paired but misaligned data have started to emerge. However, no robust and
well-performing methods applicable to a wide range of real world data sets
exist. In this work, we propose a generic solution to the problem of
cross-modality image synthesis with paired but non-aligned data by introducing
new deformation equivariance encouraging loss functions. The method consists of
joint training of an …

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