# Integrating uncertainty in deep neural networks for MRI based stroke analysis

arXiv: https://arxiv.org/abs/2008.06332 · HTML: https://mlsanity.com/paper/2008.06332/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 12 of 12)

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- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Uncertainty categories in medical image segmentation: a study of source-related diversity](https://mlsanity.com/paper/2203.00238/) (2022)
- [Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation](https://mlsanity.com/paper/2411.15490/) (2024)
- [Uncertainty-Based Biological Age Estimation of Brain MRI Scans](https://mlsanity.com/paper/2103.08491/) (2021)
- [Test-Time Mixup Augmentation for Data and Class-Specific Uncertainty Estimation in Deep Learning Image Classification](https://mlsanity.com/paper/2212.00214/) (2022)
- [Single-shot Bayesian approximation for neural networks](https://mlsanity.com/paper/2308.12785/) (2023)
- [Bayesian uncertainty estimation improves clinical decision making in medical AI agents](https://mlsanity.com/paper/2607.20582/) (2026)
- [Multimodal Deep Learning for Uncertainty-Aware Radiation Pneumonitis Risk Prediction](https://mlsanity.com/paper/2608.28937/) (2026)

## Abstract (arXiv)

At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the models uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the single image-level predictions. Those methods take advantage of the uncertainty in image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.