# Robust T-Loss for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2306.00753 · HTML: https://mlsanity.com/paper/2306.00753/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Normalized Loss Functions for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2006.13554/) (2020)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning](https://mlsanity.com/paper/2006.07805/) (2020)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Adaptive Early-Learning Correction for Segmentation from Noisy Annotations](https://mlsanity.com/paper/2110.03740/) (2021)
- [Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network](https://mlsanity.com/paper/2105.13001/) (2021)
- [Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation](https://mlsanity.com/paper/2107.10100/) (2021)
- [Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation](https://mlsanity.com/paper/1803.01199/) (2018)
- [Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation](https://mlsanity.com/paper/1906.02415/) (2019)

## Built on by (most cited first, 14 of 14)

- [Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook](https://mlsanity.com/paper/2312.05391/) (2023)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [D-MASTER: Mask Annealed Transformer for Unsupervised Domain Adaptation in Breast Cancer Detection from Mammograms](https://mlsanity.com/paper/2407.06585/) (2024)
- [Hyperbolic Metric Learning for Visual Outlier Detection](https://mlsanity.com/paper/2403.15260/) (2024)
- [Active Negative Loss: A Robust Framework for Learning with Noisy Labels](https://mlsanity.com/paper/2412.02373/) (2024)
- [A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions](https://mlsanity.com/paper/2504.14795/) (2025)
- [Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification](https://mlsanity.com/paper/2411.17571/) (2024)
- [Weakly-supervised Mamba-Based Mastoidectomy Shape Prediction for Cochlear Implant Surgery Using 3D T-Distribution Loss](https://mlsanity.com/paper/2505.18368/) (2025)
- [Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets](https://mlsanity.com/paper/2505.20928/) (2025)
- [HyperSORT: Self-Organising Robust Training with hyper-networks](https://mlsanity.com/paper/2506.21430/) (2025)
- [From Preoperative CT to Postmastoidectomy Mesh Construction: Mastoidectomy Shape Prediction for Cochlear Implant Surgery](https://mlsanity.com/paper/2601.04405/) (2026)
- [Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation](https://mlsanity.com/paper/2601.14039/) (2026)
- [HadBalance: A Plug-and-Play Unified Global Geometric Prior Framework for Generalizable Biomedical Segmentation](https://mlsanity.com/paper/2606.15976/) (2026)
- [Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels](https://mlsanity.com/paper/2610.05918/) (2026)

## Abstract (arXiv)

This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. This parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website can be found at https://robust-tloss.github.io