Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy'
image from a pathological one, could be helpful in tasks such as anomaly
detection, understanding changes induced by pathology and disease or even as
data augmentation. We treat this task as a factor decomposition problem: we aim
to separate what appears to be healthy and where disease is (as a map)...
factors are then recombined (by a network) to reconstruct the input disease
image. We train our models in an adversarial way using either paired or
unpaired settings, where we pair disease images and maps (as segmentation
masks) when available. We quantitatively evaluate the quality of pseudo healthy
images. We show in a series of experiments, performed in ISLES and BraTS
datasets, that our method is better than conditional GAN and CycleGAN,
highlighting challenges in using adversarial methods in the image translation
task of pseudo healthy image generation.