An asymmetric cycle-consistency loss for dealing with many-to-one mappings in image translation: A study on thigh mr scans - Gadermayr, M.; Tschuchnig, M.; Gupta, L.; Kraemer, N.; Truhn, Daniel; Merhof, D.; Gess, B. (Proceedings - International Symposium on Biomedical Imaging, 2021)
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.
CRIS-Team Scopus Importer:2023-03-23