Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling

Abstract

Deep neural network models for image segmentation can be a powerful tool for the automation of motor claims handling processes in the insurance industry. A crucial aspect is the reliability of the model outputs when facing adverse conditions, such as low quality photos taken by claimants to document damages. We explore the use of a meta-classification model to empirically assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. Different sets of features correlated with the quality of a segment are compared, and an AUROC score of 0.915 is achieved for distinguishing between high- and low-quality segments. By removing low-quality segments, the average mIoU of the segmentation output is improved by 16 percentage points and the number of wrongly predicted segments is reduced by 77%.

11 pages, 10 figures, 3 tables

References in corpus (7)

• XGBoost: A Scalable Tree Boosting System
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
• Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
• Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
• DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
• Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation