Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
| Autor: | Xiaoman Zhang, Zeng Zeng, Le Zhang, Xulei Yang, Wei Li, Cen Chen, Jie Wang, Songyou Peng, Ziyuan Zhao |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Focus (computing) Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) 020208 electrical & electronic engineering Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Whole systems Market segmentation 0202 electrical engineering electronic engineering information engineering ComputingMilieux_COMPUTERSANDEDUCATION 020201 artificial intelligence & image processing Segmentation Artificial intelligence business computer |
| Zdroj: | BHI |
| DOI: | 10.48550/arxiv.1903.04778 |
| Popis: | 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. Comment: IEEE BHI 2019 accepted |
| Databáze: | OpenAIRE |
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