# Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions

arXiv: https://arxiv.org/abs/1806.08640 · HTML: https://mlsanity.com/paper/1806.08640/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2018.

## Built on by (most cited first, 25 of 25)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Topology-Aware Uncertainty for Image Segmentation](https://mlsanity.com/paper/2306.05671/) (2023)
- [Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples](https://mlsanity.com/paper/2102.04150/) (2021)
- [On the relationship between calibrated predictors and unbiased volume estimation](https://mlsanity.com/paper/2112.12560/) (2021)
- [Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network](https://mlsanity.com/paper/2407.19082/) (2024)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation](https://mlsanity.com/paper/2308.07528/) (2023)
- [From Registration Uncertainty to Segmentation Uncertainty](https://mlsanity.com/paper/2403.05111/) (2024)
- [Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning](https://mlsanity.com/paper/2101.03285/) (2021)
- [Acquisition-invariant brain MRI segmentation with informative uncertainties](https://mlsanity.com/paper/2111.04094/) (2021)
- [Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection](https://mlsanity.com/paper/2206.10911/) (2022)
- [An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth](https://mlsanity.com/paper/2104.08789/) (2021)
- [Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations](https://mlsanity.com/paper/2111.11269/) (2021)
- [Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows](https://mlsanity.com/paper/2112.03277/) (2021)
- [Self-training with dual uncertainty for semi-supervised medical image segmentation](https://mlsanity.com/paper/2304.04441/) (2023)
- [Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation](https://mlsanity.com/paper/2609.10495/) (2026)
- [Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength](https://mlsanity.com/paper/2110.06435/) (2021)
- [The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties](https://mlsanity.com/paper/2111.02771/) (2021)
- [Uncertainty Detection and Reduction in Neural Decoding of EEG Signals](https://mlsanity.com/paper/2201.00627/) (2021)
- [Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution](https://mlsanity.com/paper/2508.12508/) (2025)
- [Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networks](https://mlsanity.com/paper/2603.10731/) (2026)

## Abstract (arXiv)

Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify their uncertainty, which may lead to errors in downstream analysis. In this work we propose to use Bayesian neural networks to quantify uncertainty within the domain of semantic segmentation. We also propose a method to convert voxel-wise segmentation uncertainty into volumetric uncertainty, and calibrate the accuracy and reliability of confidence intervals of derived measurements. When applied to a tumour volume estimation application, we demonstrate that by using such modelling of uncertainty, deep learning systems can be made to report volume estimates with well-calibrated error-bars, making them safer for clinical use. We also show that the uncertainty estimates extrapolate to unseen data, and that the confidence intervals are robust in the presence of artificial noise. This could be used to provide a form of quality control and quality assurance, and may permit further adoption of deep learning tools in the clinic.