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

翻译：体积测量是三维医学图像分割的主要下游应用之一，例如用于检测异常组织生长或手术规划。保形预测是一种有前景的不确定性量化框架，可为自动体积测量提供校准的预测区间。然而，该方法基于校准样本与测试样本可交换的假设，这一假设在医学图像应用中常被违背。加权保形预测的表述框架可以缓解此问题，但其在医学领域的实证研究仍显不足。一个潜在原因是它依赖于校准分布与测试分布之间密度比的估计，而在涉及高维数据的场景中，这种估计往往难以处理。为解决此问题，我们提出了一种基于分割模型生成的压缩潜在表示进行密度比估计的高效方法。实验表明，在合成和真实场景中，我们的方法能有效降低存在协变量偏移时的覆盖误差。我们的实现代码可在 https://github.com/benolmbrt/wcp_miccai 获取。