# Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

arXiv: https://arxiv.org/abs/2012.11840 · HTML: https://mlsanity.com/paper/2012.11840/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 20 of 20)

- [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)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis](https://mlsanity.com/paper/2107.04882/) (2021)
- [Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning](https://mlsanity.com/paper/2107.13508/) (2021)
- [Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods](https://mlsanity.com/paper/2104.15007/) (2021)
- [An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions](https://mlsanity.com/paper/2110.03260/) (2021)
- [Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients](https://mlsanity.com/paper/2104.08954/) (2021)
- [Confidence Aware Neural Networks for Skin Cancer Detection](https://mlsanity.com/paper/2107.09118/) (2021)
- [An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products](https://mlsanity.com/paper/2107.11643/) (2021)
- [Controlled Dropout for Uncertainty Estimation](https://mlsanity.com/paper/2205.03109/) (2022)
- [Error-Driven Uncertainty Aware Training](https://mlsanity.com/paper/2405.01205/) (2024)
- [CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment](https://mlsanity.com/paper/2505.22803/) (2025)
- [Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks](https://mlsanity.com/paper/2407.04726/) (2024)
- [Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification](https://mlsanity.com/paper/2505.15671/) (2025)
- [Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data](https://mlsanity.com/paper/2110.12464/) (2021)
- [Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images](https://mlsanity.com/paper/2309.11318/) (2023)
- [Relationship between Uncertainty in DNNs and Adversarial Attacks](https://mlsanity.com/paper/2409.13232/) (2024)
- [Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks](https://mlsanity.com/paper/2509.13338/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference.