Discriminative Consistent Domain Generation for Semi-supervised Learning

Abstract

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. * These authors contributed equally to this work.

Deep Learning for Cardiac Image Segmentation: A Review

“…Several works have successfully applied adversarial training for cross-modality segmentation tasks, adapting a cardiac segmentation model learned from MR images to CT images and vice versa (Dou et al, 2018(Dou et al, , 2019Ouyang et al, 2019;Chen et al, 2019c). These type of approaches can also be adopted for semi-supervised learning, where the target domain is a new set of unlabeled data of the same modality (Chen et al, 2019d).…”

Automatic Meniscus Segmentation Using Adversarial Learning-Based Segmentation Network with Object-Aware Map in Knee MR Images

“…Adversarial learning has recently been used for various purposes such as segmentation and domain generation [23,24]. Chen et al proposed an inter-cascaded generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images [23].…”

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

“…Previously proposed methods for LA wall segmentation include (1) manual segmentation [1,2,10], which is tedious and inefficient, (2) segmentation of the LA cavity followed by some morphological dilations for LA wall extraction [11], and (3) automated or semi-automatic LA wall segmentation, e.g., active contour based segmentation [2]. Furthermore, many automated methods have been proposed for segmenting the LA [12][13][14][15][16][17]. However, they have not yet been further applied to the quantification of the atrial scars.…”