Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis
Author
Liu, XiaofengXing, Fangxu
Stone, Maureen
Zhuo, Jiachen
Reese, Timothy
Prince, Jerry L.
El Fakhri, Georges
Woo, Jonghye
Date
2021-09-21Journal
Lecture Notes in Computer SciencePublisher
Springer NatureType
Article
Metadata
Show full item recordAbstract
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-training UDA for generative tasks, such as image synthesis, is not fully investigated. In this work, we propose a novel generative self-training (GST) UDA framework with continuous value prediction and regression objective for cross-domain image synthesis. Specifically, we propose to filter the pseudo-label with an uncertainty mask, and quantify the predictive confidence of generated images with practical variational Bayes learning. The fast test-time adaptation is achieved by a round-based alternative optimization scheme. We validated our framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis problem, where datasets in the source and target domains were acquired from different scanners or centers. Extensive validations were carried out to verify our framework against popular adversarial training UDA methods. Results show that our GST, with tagged MRI of test subjects in new target domains, improved the synthesis quality by a large margin, compared with the adversarial training UDA methods.Sponsors
National Institutes of HealthKeyword
generative self-training (GST)unsupervised domain adaptation
Artificial Intelligence
Machine Learning
Identifier to cite or link to this item
http://hdl.handle.net/10713/16877ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-87199-4_13