High-Resolution Hierarchical Adversarial Learning for OCT Speckle Noise Reduction

Jiang Li

Abstract:

Data annotation is always an expensive and time-consuming
issue for deep learning based medical image analysis. To ease the need of
annotations, domain adaptation is recently introduced to generalize neural
networks from a labeled source domain to unlabeled target domain
without much performance degradation. In this paper, we propose a novel
target domain self-supervision for domain adaptation by constructing an
edge generation auxiliary task to assist primary segmentation task so as
to extract better target representation and improve target segmentation
performance. Besides, in order to leverage detailed information contained
in low-level features, we propose a hierarchical low-level adversarial learning
mechanism to encourage low-level features domain uninformative in a
hierarchical way, so that the segmentation performance can benefit from
low-level features without being affected by domain shift. Following these
two proposed approach, we develop a cross-modality domain adaptation
framework which employs the dual-task collaboration for target domain
self-supervision, and encourages low-level detailed features domain uninformative
for better alignment. Our proposed framework achieves stateof-
the-art results on public cross-modality segmentation datasets.