通过不确定性监督提升医学图像分割的可解释性与鲁棒性
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
- 基于边界梯度与噪声关系设计不确定性监督损失
- 在分布外场景下显著提升预测鲁棒性，且保持良好分割性能
- 适合关注模型可信度与临床可解释性的研究者
不确定性估计在医学图像分割中被广泛用于评估结果可靠性，尤其在深度学习方法中。然而，以往方法普遍缺乏对不确定性的有效监督，导致预测结果可解释性差、鲁棒性不足。本文提出一种自监督方法来指导不确定性学习，引入关于不确定性与边界附近图像梯度及噪声关系的三个原则，并据此设计两种不确定性监督损失。这些损失增强了模型预测与人类判断的一致性。同时，我们提出了新的定量指标，用于评估不确定性的可解释性与鲁棒性。实验表明，相较于现有最优方法，该方法在分布外（OOD）场景下表现更优，且显著提升了不确定性估计的可解释性与鲁棒性。代码已开源：https://github.com/suiannaius/SURE。
原文摘要 · Abstract (English)
Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability, particularly in deep learning approaches. However, previous methods generally lack effective supervision in uncertainty estimation, leading to low interpretability and robustness of the predictions. In this work, we propose a self-supervised approach to guide the learning of uncertainty. Specifically, we introduce three principles about the relationships between the uncertainty and the image gradients around boundaries and noise. Based on these principles, two uncertainty supervision losses are designed. These losses enhance the alignment between model predictions and human interpretation. Accordingly, we introduce novel quantitative metrics for evaluating the interpretability and robustness of uncertainty. Experimental results demonstrate that compared to state-of-the-art approaches, the proposed method can achieve competitive segmentation performance and superior results in out-of-distribution (OOD) scenarios while significantly improving the interpretability and robustness of uncertainty estimation. Code is available via https://github.com/suiannaius/SURE.
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