# Robust Conformal Volume Estimation in 3D Medical Images

arXiv: https://arxiv.org/abs/2407.19938 · HTML: https://mlsanity.com/paper/2407.19938/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019)
- [Conformal prediction beyond exchangeability](https://mlsanity.com/paper/2202.13415/) (2022)
- [Conformal Risk Control](https://mlsanity.com/paper/2208.02814/) (2022)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A Conformal Prediction Approach to Explore Functional Data](https://mlsanity.com/paper/1302.6452/) (2013)
- [The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma](https://mlsanity.com/paper/2305.07642/) (2023)
- [Optimized U-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2110.03352/) (2021)
- [On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging](https://mlsanity.com/paper/2309.01488/) (2023)
- [Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss](https://mlsanity.com/paper/1909.10670/) (2019)
- [Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation](https://mlsanity.com/paper/2308.03723/) (2023)
- [TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images](https://mlsanity.com/paper/2307.15638/) (2023)

## Built on by (most cited first, 4 of 4)

- [COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics](https://mlsanity.com/paper/2509.22240/) (2025)
- [Conformal forecasting for surgical instrument trajectory](https://mlsanity.com/paper/2503.04191/) (2025)
- [Trustworthy Endoscopic Super-Resolution](https://mlsanity.com/paper/2604.18001/) (2026)
- [The Exceedance Design Effect: Effective Sample Size for Thresholds under Clustering](https://mlsanity.com/paper/2608.21262/) (2026)

## Abstract (arXiv)

Volumetry is one of the principal downstream applications of 3D medical image segmentation, for example, to detect abnormal tissue growth or for surgery planning. Conformal Prediction is a promising framework for uncertainty quantification, providing calibrated predictive intervals associated with automatic volume measurements. However, this methodology is based on the hypothesis that calibration and test samples are exchangeable, an assumption that is in practice often violated in medical image applications. A weighted formulation of Conformal Prediction can be framed to mitigate this issue, but its empirical investigation in the medical domain is still lacking. A potential reason is that it relies on the estimation of the density ratio between the calibration and test distributions, which is likely to be intractable in scenarios involving high-dimensional data. To circumvent this, we propose an efficient approach for density ratio estimation relying on the compressed latent representations generated by the segmentation model. Our experiments demonstrate the efficiency of our approach to reduce the coverage error in the presence of covariate shifts, in both synthetic and real-world settings. Our implementation is available at https://github.com/benolmbrt/wcp_miccai