Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection

TL;DR

This paper introduces a novel quantile regression approach for uncertainty estimation in lesion detection, applicable to both supervised and unsupervised settings, improving outlier detection and boundary uncertainty characterization.

Contribution

It proposes QR-VAE for unsupervised uncertainty quantification and BQR for supervised lesion segmentation, addressing variance underestimation and capturing boundary disagreement.

Findings

QR-VAE effectively estimates uncertainty without variance shrinkage.

BQR captures boundary uncertainty aligning with expert disagreement.

Method improves lesion detection reliability in critical applications.

Abstract

Despite impressive state-of-the-art performance on a wide variety of machine learning tasks, deep learning methods can produce over-confident predictions, particularly with limited training data. Therefore, quantifying uncertainty is particularly important in critical applications such as lesion detection and clinical diagnosis, where a realistic assessment of uncertainty is essential in determining surgical margins, disease status and appropriate treatment. In this work, we propose a novel approach that uses quantile regression for quantifying aleatoric uncertainty in both supervised and unsupervised lesion detection problems. The resulting confidence intervals can be used for lesion detection and segmentation. In the unsupervised setting, we combine quantile regression with the Variational AutoEncoder (VAE). Here we address the problem of quantifying uncertainty in the images that are reconstructed by the VAE as the basis for principled outlier or lesion detection. The VAE models the output as a conditionally independent Gaussian characterized by its mean and variance. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. Here we describe an alternative Quantile-Regression VAE (QR-VAE) that avoids this variance shrinkage problem by directly estimating conditional quantiles for the input image. Using the estimated quantiles, we compute the conditional mean and variance for the input image from which we then detect outliers by thresholding at a false-discovery-rate corrected p-value. In the supervised setting, we develop binary quantile regression (BQR) for the supervised lesion segmentation task. We show how BQR can be used to capture uncertainty in lesion boundaries in a manner that characterizes expert disagreement.