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Adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a high potential in medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks. We offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss. We show formally and empirically that the proposed method improves the performance without radically changing the architecture and increasing the model complexity.

An Asymmetric Cycle-Consistency Loss For Dealing With Many-To-One Mappings In Image Translation: A Study On Thigh Mr Scans