Conformal coronary calcification volume estimation with conditional coverage via histogram clustering
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910987110580224 |
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| author | Jaubert, Olivier Mohammadi, Salman Goatman, Keith A. Mikhael, Shadia S. Bradley, Conor Hughes, Rebecca Good, Richard Hipwell, John H. Dahdouh, Sonia |
| author_facet | Jaubert, Olivier Mohammadi, Salman Goatman, Keith A. Mikhael, Shadia S. Bradley, Conor Hughes, Rebecca Good, Richard Hipwell, John H. Dahdouh, Sonia |
| contents | Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. However, over-reporting could negatively affect patient wellbeing and unnecessarily burden the medical system. Therefore, careful considerations should be taken when automatically reporting coronary calcium scores. A cluster-based conditional conformal prediction framework is proposed to provide score intervals with calibrated coverage from trained segmentation networks without retraining. The proposed method was tuned and used to calibrate predictive intervals for 3D UNet models (deterministic, MCDropout and deep ensemble) reaching similar coverage with better triage metrics compared to conventional conformal prediction. Meaningful predictive intervals of calcium scores could help triage patients according to the confidence of their risk category prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal coronary calcification volume estimation with conditional coverage via histogram clustering Jaubert, Olivier Mohammadi, Salman Goatman, Keith A. Mikhael, Shadia S. Bradley, Conor Hughes, Rebecca Good, Richard Hipwell, John H. Dahdouh, Sonia Image and Video Processing Computer Vision and Pattern Recognition Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. However, over-reporting could negatively affect patient wellbeing and unnecessarily burden the medical system. Therefore, careful considerations should be taken when automatically reporting coronary calcium scores. A cluster-based conditional conformal prediction framework is proposed to provide score intervals with calibrated coverage from trained segmentation networks without retraining. The proposed method was tuned and used to calibrate predictive intervals for 3D UNet models (deterministic, MCDropout and deep ensemble) reaching similar coverage with better triage metrics compared to conventional conformal prediction. Meaningful predictive intervals of calcium scores could help triage patients according to the confidence of their risk category prediction. |
| title | Conformal coronary calcification volume estimation with conditional coverage via histogram clustering |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.04030 |