Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917712021684224 |
|---|---|
| author | Skärström, Victor Wåhlstrand Johansson, Lisa Alvén, Jennifer Lorentzon, Mattias Häggström, Ida |
| author_facet | Skärström, Victor Wåhlstrand Johansson, Lisa Alvén, Jennifer Lorentzon, Mattias Häggström, Ida |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02926 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification Skärström, Victor Wåhlstrand Johansson, Lisa Alvén, Jennifer Lorentzon, Mattias Häggström, Ida Image and Video Processing Computer Vision and Pattern Recognition I.4.8; I.2.10; J.3 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 |
| topic | Image and Video Processing Computer Vision and Pattern Recognition I.4.8; I.2.10; J.3 |
| url | https://arxiv.org/abs/2407.02926 |