How to Cure Cancer (in images) with Unpaired Image Translation

Joseph Paul Cohen, Margaux Luck, Sina Honari

Apr 11, 2018 MIDL 2018 Abstract Submission readers: everyone
  • Abstract: We discuss how distribution matching losses, such as those used in CycleGAN, when used to translate images from one domain to another can lead to mis-diagnosis of medical conditions. It seems appealing to use these methods for image translation from the source domain to the target domain without requiring paired data. However, the way these models function is through matching the distribution of the translated images to the target domain. This can cause issues especially when the percentage of known and unknown labels (e.g. sick and healthy labels) differ between the source and target domains. When the output of the model is an image, current methods do not guarantee that the known and unknown labels have been preserved. Therefore until alternative solutions are proposed to maintain the accuracy of the translated features, such translated images should not be used for medical interpretation (e.g. by doctors). However, recent papers are using these models as if this is the goal.
  • Author affiliation: Montreal Institute for Learning Algorithms
  • Keywords: distribution matching, MRI, adversarial loss
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