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arXiv 2004.110012021-01-11eess.IVcs.CVcs.LG

An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；RWTH Aachen University（亚琛工业大学）；University Hospital Aachen（亚琛大学医院）

Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof, Nils Krämer, Daniel Truhn, Burkhard Gess

备注：: Presented at IEEE ISBI'21

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Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold 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. In this work, 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 significantly without radically changing the architecture and without increasing the overall complexity. We evaluate our method on thigh MRI scans with the final goal of segmenting the muscle in fat-infiltrated patients' data.

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