Domain Adaptation for Medical Image Segmentation using
  Transformation-Invariant Self-Training
Negin Ghamsarian Javier Gamazo Tejero Pablo Márquez Neila Sebastian Wolf Martin Zinkernagel Klaus Schoeffmann Raphael Sznitman
Models capable of leveraging unlabelled data are crucial in overcoming large
distribution gaps between the acquired datasets across different imaging
devices and configurations. In this regard, self-training techniques based on
pseudo-labeling have been shown to be highly effective for semi-supervised
domain adaptation. However, the unreliability of pseudo labels can hinder the
capability of self-training techniques to induce abstract representation from
the unlabeled target dataset, especially in the case of large distribution
gaps. Since the neural network performance should be invariant to image
transformations, we look to this fact to identify uncertain pseudo labels.
Indeed, we argue that transformation invariant detections can provide more
reasonable approximations of ground truth. Accordingly, we propose a
semi-supervised learning strategy for domain adaptation termed
transformation-invariant self-training (TI-ST). The proposed method assesses
pixel-wise pseudo-labels' reliability and filters out unreliable detections
during self-training. We perform comprehensive evaluations for domain
adaptation using three different modalities of medical images, two different
network architectures, and several alternative state-of-the-art domain
adaptation methods. Experimental results confirm the superiority of our
proposed method in mitigating the lack of target domain annotation and boosting
segmentation performance in the target domain.