A Dual-stage Teacher-Student Representation Learning Framework for Semi-supervised Cardiac Image Segmentation

This study introduces a novel generative adversarial network (GAN)-based Dual-stage Teacher-Student Representation Learning (GDL) framework designed to extract effective representations from unlabeled data for cardiac image segmentation. The GDL framework employs an adversarial augmentation approach, simultaneously training a generation model to produce diverse samples and a teacher-student model to learn image representations iteratively. During the pre-training stage, a generator is trained using a diversity-enhanced contrastive loss to generate diverse augmented samples. Subsequently, in the first stage, the generator is further supervised by enforcing dissimilarity between representations produced by the teacher and student models, aiming to expand the image representation space of generated images. In the second stage, the student model is trained to transfer distilled knowledge acquired from the augmented samples to the teacher model to improve its generalization performance in the image segmentation task. Our proposed model ensures that the diverse generated samples correspond precisely to their pseudo-labels, facilitating learning semantic representations from unlabeled data. Experimental results on public cardiac MR datasets demonstrate that our model surpasses the performance of state-of-the-art semi-supervised learning networks.