We develop confidence sets which provide spatial uncertainty guarantees for the output of a black-box machine learning model designed for image segmentation. To do so we adapt conformal inference to the imaging setting, obtaining thresholds on a calibration dataset based on the distribution of the maximum of the transformed logit scores within and outside of the ground truth masks. We prove that these confidence sets, when applied to new predictions of the model, are guaranteed to contain the true unknown segmented mask with desired probability. We show that learning appropriate score transformations on a learning dataset before performing calibration is crucial for optimizing performance. We illustrate and validate our approach on a polpys tumor dataset. To do so we obtain the logit scores from a deep neural network trained for polpys segmentation and show that using distance transformed scores to obtain outer confidence sets and the original scores for inner confidence sets enables tight bounds on tumor location whilst controlling the false coverage rate.

本研究针对生物医学图像分割中的空间不确定性问题，提出了一种新的符合性置信集方法，能够为黑盒机器学习模型的输出提供置信保证。研究结果表明，学习适当的得分变换对优化模型性能至关重要，实验验证了该方法在肠息肉肿瘤数据集上的有效性，能够实现肿瘤位置的精确界定并控制错误覆盖率。

生物医学图像分割的符合性置信集