# Semi-Supervised Deep Learning for Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1703.06000 · HTML: https://mlsanity.com/paper/1703.06000/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [Uncertainty guided semi-supervised segmentation of retinal layers in OCT images](https://mlsanity.com/paper/2103.02083/) (2021)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)
- [Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.11629/) (2021)
- [Belief function-based semi-supervised learning for brain tumor segmentation](https://mlsanity.com/paper/2102.00097/) (2021)
- [Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://mlsanity.com/paper/2109.10902/) (2021)
- [FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.00400/) (2022)
- [Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/2109.09596/) (2021)
- [Classification Committee for Active Deep Object Detection](https://mlsanity.com/paper/2308.08476/) (2023)
- [Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series](https://mlsanity.com/paper/2310.03870/) (2023)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)
- [Marine Debris Detection in Satellite Surveillance using Attention Mechanisms](https://mlsanity.com/paper/2307.04128/) (2023)

## Abstract (arXiv)

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.