# Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis

arXiv: https://arxiv.org/abs/2005.11797 · HTML: https://mlsanity.com/paper/2005.11797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 1 of 1)

- [Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection](https://mlsanity.com/paper/2104.14029/) (2021)

## Abstract (arXiv)

Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration. In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.