Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Abstract

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.

IEEE BHI 2019 accepted

References in corpus (2)

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• PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

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