  Deep learning based task systems normally rely on a large amount of manually
labeled training data, which is expensive to obtain and subject to operator
variations. Moreover, it does not always hold that the manually labeled data
and the unlabeled data are sitting in the same distribution. In this paper, we
alleviate these problems by proposing a discriminative consistent domain
generation (DCDG) approach to achieve a semi-supervised learning. The
discriminative consistent domain is achieved by a double-sided domain
adaptation. The double-sided domain adaptation aims to make a fusion of the
feature spaces of labeled data and unlabeled data. In this way, we can fit the
differences of various distributions between labeled data and unlabeled data.
In order to keep the discriminativeness of generated consistent domain for the
task learning, we apply an indirect learning for the double-sided domain
adaptation. Based on the generated discriminative consistent domain, we can use
the unlabeled data to learn the task model along with the labeled data via a
consistent image generation. We demonstrate the performance of our proposed
DCDG on the late gadolinium enhancement cardiac MRI (LGE-CMRI) images acquired
from patients with atrial fibrillation in two clinical centers for the
segmentation of the left atrium anatomy (LA) and proximal pulmonary veins
(PVs). The experiments show that our semi-supervised approach achieves
compelling segmentation results, which can prove the robustness of DCDG for the
semi-supervised learning using the unlabeled data along with labeled data
acquired from a single center or multicenter studies.
