# Semi-supervised Learning with Robust Loss in Brain Segmentation

arXiv: https://arxiv.org/abs/2212.03082 · HTML: https://mlsanity.com/paper/2212.03082/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 6 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Semi-supervised Learning using Robust Loss](https://mlsanity.com/paper/2203.01524/) (2022)
- [Learning from imperfect training data using a robust loss function: application to brain image segmentation](https://mlsanity.com/paper/2208.04941/) (2022)

## Abstract (arXiv)

In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images. The semi-supervised model uses less labeled data, and the performance is competitive with the supervised model with full labeled data. This framework could reduce the cost of labeling MRI images. We also introduced robust loss to reduce the noise effects of inaccurate labels generated in semi-supervised learning.