Adversarial Pseudo Healthy Synthesis Needs Pathology FactorizationDownload PDF

Dec 13, 2018 (edited Jun 24, 2019)MIDL 2019 Conference Full SubmissionReaders: Everyone
  • Keywords: pseudo healthy synthesis, GAN, cycle-consistency, factorization
  • TL;DR: We propose a pseudo healthy synthesis method using adversarial learning and pathology factorization.
  • Abstract: 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). The two 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.
  • Code Of Conduct: I have read and accept the code of conduct.
  • Remove If Rejected: (optional) Remove submission if paper is rejected.
7 Replies

Loading