# Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans

arXiv: https://arxiv.org/abs/1909.08959 · HTML: https://mlsanity.com/paper/1909.08959/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets](https://mlsanity.com/paper/2505.20928/) (2025)
- [Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation](https://mlsanity.com/paper/2601.14039/) (2026)

## Abstract (arXiv)

Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications. Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance. In real-world scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task. This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited. In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task. Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.