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

arXiv: https://arxiv.org/abs/1903.04778 · HTML: https://mlsanity.com/paper/1903.04778/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation](https://mlsanity.com/paper/2101.09057/) (2021)
- [LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://mlsanity.com/paper/2212.02078/) (2022)
- [Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/2105.10369/) (2021)
- [MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation](https://mlsanity.com/paper/2207.01883/) (2022)
- [Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes](https://mlsanity.com/paper/2310.10352/) (2023)
- [Learning Generalizable Features for Tibial Plateau Fracture Segmentation Using Masked Autoencoder and Limited Annotations](https://mlsanity.com/paper/2502.02862/) (2025)

## Abstract (arXiv)

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.