Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
This addresses medical image segmentation for healthcare applications, but it appears incremental as it builds on existing semi-supervised and self-taught learning concepts.
The paper tackled finger bone segmentation by introducing a semi-supervised self-taught deep learning framework with a student-teacher setup, achieving superior results over conventional supervised methods.
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.