Attentive Continuous Generative Self-training for Unsupervised Domain
  Adaptive Medical Image Translation
Xiaofeng Liu Jerry L. Prince Fangxu Xing Jiachen Zhuo Reese Timothy Maureen Stone Georges El Fakhri Jonghye Woo
Self-training is an important class of unsupervised domain adaptation (UDA)
approaches that are used to mitigate the problem of domain shift, when applying
knowledge learned from a labeled source domain to unlabeled and heterogeneous
target domains. While self-training-based UDA has shown considerable promise on
discriminative tasks, including classification and segmentation, through
reliable pseudo-label filtering based on the maximum softmax probability, there
is a paucity of prior work on self-training-based UDA for generative tasks,
including image modality translation. To fill this gap, in this work, we seek
to develop a generative self-training (GST) framework for domain adaptive image
translation with continuous value prediction and regression objectives.
Specifically, we quantify both aleatoric and epistemic uncertainties within our
GST using variational Bayes learning to measure the reliability of synthesized
data. We also introduce a self-attention scheme that de-emphasizes the
background region to prevent it from dominating the training process. The
adaptation is then carried out by an alternating optimization scheme with
target domain supervision that focuses attention on the regions with reliable
pseudo-labels. We evaluated our framework on two cross-scanner/center,
inter-subject translation tasks, including tagged-to-cine magnetic resonance
(MR) image translation and T1-weighted MR-to-fractional anisotropy translation.
Extensive validations with unpaired target domain data showed that our GST
yielded superior synthesis performance in comparison to adversarial training
UDA methods.