An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
By M. Gadermayr, M. Tschuchnig, Dorit Merhof, Nils Krämer, D. Truhn, B. Gess. 2020
5 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Cancer Research, Computer Vision and Pattern Recognition
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Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, background): “2) Providing more variance information such as pitch, duration, and energy as decoder input, which eases the one-to-many mapping problem [136, 81, 376, 449] in text to speech12.”
- FastSpeech 2: Fast and High-Quality End-to-End Text to Speech (2020, background): “TTS is a typical one-to-many mapping problem [4, 6, 29], since multiple possible speech sequences can correspond to a text sequence due to different variations in speech audio, such as pitch, duration, sound volume and prosody.”