Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.
@article{arxiv.1903.04778,
title = {Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation},
author = {Ziyuan Zhao and Xiaoman Zhang and Cen Chen and Wei Li and Songyou Peng and Jie Wang and Xulei Yang and Le Zhang and Zeng Zeng},
journal= {arXiv preprint arXiv:1903.04778},
year = {2022}
}