为医学图像分割提供有概率保证的不确定性区域，提升临床可信度。
Conformal confidence sets for biomedical image segmentation
- 用校准数据集调整得分分布，生成空间上可保证的置信区域。
- 在息肉数据集上验证，置信集覆盖真实病灶率达指定概率，误覆盖率可控。
- 适合关注模型可靠性与医疗决策安全性的研究人员使用。
我们开发了针对黑箱机器学习模型在图像分割输出上提供空间不确定性保证的置信集。通过将等变推断方法适配至医学影像场景，基于校准数据集上真值掩码内外最大变换后对数几率分数的分布确定阈值。证明当应用于新预测时，该置信集以期望概率包含真实的未知分割掩码。我们表明，在校准前于学习数据集上学习合适的得分变换对性能优化至关重要。在息肉肿瘤数据集上进行验证：从训练好的深度神经网络获取对数几率分数，采用距离变换得分生成外置置信集，原始得分用于内置置信集，实现对肿瘤位置的紧致边界并控制误覆盖率。
原文摘要 · Abstract (English)
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.
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