用形态学构造分割置信集，实现可验证的不确定性量化。
Conformal Prediction for Image Segmentation Using Morphological Prediction Sets
- 通过形态学膨胀构建预测掩码边界扩展区域
- 在95%置信度下，真实标签被包含于预测集内
- 无需模型反馈，适配任意分割模型，尤其医疗影像
图像分割受多种不确定性影响，如标注过程或训练数据采样。本文聚焦二值分割，采用共形预测（conformal prediction）方法进行不确定性量化，该方法具有有限样本理论保证且对模型和数据无依赖。我们基于校准数据计算非一致性得分（nonconformity scores），即预测残差，并利用数学形态学中的膨胀操作，在预测分割掩码边缘添加边界扩展区域。推理时，由掩码及其扩展区域构成的预测集以用户指定的置信水平（如95%）包含真实标签。扩展区域大小反映模型在特定数据集上的预测不确定性。本方法仅需预测掩码，无需模型反馈，适用于任意分割模型，包括深度学习模型。我们在多个医学影像任务上验证了该方法的有效性。
原文摘要 · Abstract (English)
Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as the data labeling process or the sampling of training data. In this paper we focus on binary segmentation and address these challenges using conformal prediction, a family of model- and data-agnostic methods for uncertainty quantification that provide finite-sample theoretical guarantees and applicable to any pretrained predictor. Our approach involves computing nonconformity scores, a type of prediction residual, on held-out calibration data not used during training. We use dilation, one of the fundamental operations in mathematical morphology, to construct a margin added to the borders of predicted segmentation masks. At inference, the predicted set formed by the mask and its margin contains the ground-truth mask with high probability, at a confidence level specified by the user. The size of the margin serves as an indicator of predictive uncertainty for a given model and dataset. We work in a regime of minimal information as we do not require any feedback from the predictor: only the predicted masks are needed for computing the prediction sets. Hence, our method is applicable to any segmentation model, including those based on deep learning; we evaluate our approach on several medical imaging applications.
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