Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification
<|reference_start|>Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification: 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.<|reference_end|>
@article{skärström2024explainable,
    title={Explainable vertebral fracture analysis with uncertainty estimation
  using differentiable rule-based classification},
    author={Victor W{aa}hlstrand Sk"arstr"om, Lisa Johansson, Jennifer Alv'en,
  Mattias Lorentzon, Ida H"aggstr"om},
    journal={arXiv preprint arXiv:2407.02926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02926},
    primaryClass={eess.IV cs.CV}
}