bibtext: @InProceedings{ Wåh_Explainable_MICCAI2024,
                   author = { Wåhlstrand Skärström, Victor and Johansson, Lisa and Alvén, Jennifer and Lorentzon, Mattias and Häggström, Ida },
                   title = { { Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: We present a novel method for explainable vertebral fracture assessment (XVFA) in low-dose radiographs using deep neural networks, incorporating vertebra detection and keypoint localization with uncertainty estimates. We incorporate Genant’s semi-quantitative criteria as a differentiable rule-based means of classifying both vertebra fracture grade and morphology. Unlike previous work, XVFA provides explainable classifications relatable to current clinical methodology, as well as uncertainty estimations, while at the same time surpassing  state-of-the art methods with a vertebra-level sensitivity of 93% and end-to-end AUC of 97% in a challenging setting. Moreover, we compare intra-reader agreement with model uncertainty estimates, with model reliability on par with human annotators.
title: Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification
authors: Wåhlstrand Skärström, Victor Johansson, Lisa Alvén, Jennifer Lorentzon, Mattias Häggström, Ida