Papers › Robust Conformal Volume Estimation in 3D Medical Images

Robust Conformal Volume Estimation in 3D Medical Images

29 Jul 2024arXiv:2407.19938archive 2025-07-28

Benjamin Lambert, Florence Forbes, Senan Doyle, Michel Dojat

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

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1ran · our draft was wrong
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getActivation benolmbrt/wcp_miccai/feature_extractor.py official repository ran · our draft was wrong no licence file found · pointer only · d7fb24e5fb925bec · report
get_weighted_quantile benolmbrt/wcp_miccai/weighted_cp.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · a937c4fd4ff3b208 · report
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Tasks

Conformal PredictionDensity Ratio EstimationImage SegmentationMedical Image SegmentationSemantic SegmentationUncertainty Quantification

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