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

## TL;DR

This paper introduces a semi-supervised self-taught deep learning framework for finger bones segmentation, leveraging a teacher-student model that improves accuracy with unlabeled data in a lifelong learning setup.

## Contribution

It presents a novel semi-supervised self-taught approach with a teacher-student architecture for improved finger bones segmentation.

## Key findings

- Outperforms traditional supervised methods
- Effective use of unlabeled data in segmentation
- Demonstrates robustness in lifelong learning setting

## 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.