Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

TL;DR

This paper introduces an uncertainty-aware deep kernel learning model combining CNNs and Gaussian Processes for medical image analysis, providing reliable uncertainty estimates and improved performance over existing methods.

Contribution

It presents a novel model that estimates predictive uncertainty analytically in medical imaging, enhancing accuracy and confidence calibration.

Findings

Outperforms common architectures in Bone Age Prediction and Lesion Localization

Expresses higher confidence in accurate predictions and lower in uncertain cases

More computationally efficient than Monte-Carlo Dropout-based methods

Abstract

Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process. Furthermore, we adapt different pre-training methods to investigate their impacts on the proposed model. We apply our approach to Bone Age Prediction and Lesion Localization. In most cases, the proposed model shows better performance compared to common architectures. More importantly, our model expresses systematically higher confidence in more accurate predictions and less confidence in less accurate ones. Our model can also be used to detect challenging and controversial test samples. Compared to related methods such as Monte-Carlo Dropout, our approach derives the uncertainty information in a purely analytical fashion and is thus computationally more efficient.