# Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection

arXiv: https://arxiv.org/abs/2104.14029 · HTML: https://mlsanity.com/paper/2104.14029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [SelectiveNet: A Deep Neural Network with an Integrated Reject Option](https://mlsanity.com/paper/1901.09192/) (2019)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Deep Gamblers: Learning to Abstain with Portfolio Theory](https://mlsanity.com/paper/1907.00208/) (2019)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19](https://mlsanity.com/paper/2007.02202/) (2020)
- [Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases](https://mlsanity.com/paper/1908.00792/) (2019)
- [A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention](https://mlsanity.com/paper/2009.12027/) (2020)
- [Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/2007.10800/) (2020)
- [Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis](https://mlsanity.com/paper/2005.11797/) (2020)

## Abstract (arXiv)

Effective and reliable screening of patients via Computer-Aided Diagnosis can play a crucial part in the battle against COVID-19. Most of the existing works focus on developing sophisticated methods yielding high detection performance, yet not addressing the issue of predictive uncertainty. In this work, we introduce uncertainty estimation to detect confusing cases for expert referral to address the unreliability of state-of-the-art (SOTA) DNNs on COVID-19 detection. To the best of our knowledge, we are the first to address this issue on the COVID-19 detection problem. In this work, we investigate a number of SOTA uncertainty estimation methods on publicly available COVID dataset and present our experimental findings. In collaboration with medical professionals, we further validate the results to ensure the viability of the best performing method in clinical practice.