Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation

TL;DR

This paper introduces a scalable calibration framework for deep learning ensembles that improves uncertainty quantification in medical image segmentation, enhancing model reliability and aiding active learning.

Contribution

The authors propose a novel calibration method for deep ensembles that better approximates classification probabilities in high-dimensional medical imaging tasks.

Findings

Improved calibration accuracy on unseen test data.

Enhanced sensitivity and precision in two out of three cases.

Effective use of calibrated ensembles in active learning scenarios.

Abstract

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we show that these approaches fail to approximate the classification probability. On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability. On unseen test data, we demonstrate improved calibration, sensitivity (in two out of three cases) and precision when being compared with the standard approaches. We further motivate the usage of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.