针对医学影像分割的不确定性分布不均问题，提出分区域渐进优化方法。
Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation
- 按确定性分区域渐进训练，先学确定区再学不确定边界区
- 在脑转移瘤和非转移瘤分割中，所有亚区域性能均优于传统方法
- 引入模糊标签机制，稳定梯度并扩大损失函数平坦区
医学图像分割中的不确定性具有内在非均匀性，边界区域的模糊性显著高于内部区域。传统训练对所有像素同等对待，在预测不可靠的早期阶段导致优化不稳定。我们提出一种基于区域的课程学习策略，优先从确定区域学习，逐步引入不确定区域，以降低梯度方差。方法上，设计了一种帕累托一致损失，通过自适应重塑损失空间，平衡各区域间的不确定性权衡，并约束内部与边界区域的收敛动态；引导模型逼近帕累托最优解。为缓解边界模糊性，进一步开发了模糊标签机制，在非边界区域保持二值置信度，同时在边界附近实现平滑过渡，从而稳定梯度并扩展损失表面的平坦区域。在脑转移瘤与非转移瘤分割数据集上的实验表明，多种配置下性能均持续提升，本方法在所有肿瘤亚区域的表现均优于传统硬标签方法。
原文摘要 · Abstract (English)
Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior areas. Conventional training treats all pixels equally, leading to unstable optimization during early epochs when predictions are unreliable. We argue that this instability hinders convergence toward Pareto-optimal solutions and propose a region-wise curriculum strategy that prioritizes learning from certain regions and gradually incorporates uncertain ones, reducing gradient variance. Methodologically, we introduce a Pareto-consistent loss that balances trade-offs between regional uncertainties by adaptively reshaping the loss landscape and constraining convergence dynamics between interior and boundary regions; this guides the model toward Pareto-approximate solutions. To address boundary ambiguity, we further develop a fuzzy labeling mechanism that maintains binary confidence in non-boundary areas while enabling smooth transitions near boundaries, stabilizing gradients, and expanding flat regions in the loss surface. Experiments on brain metastasis and non-metastatic tumor segmentation show consistent improvements across multiple configurations, with our method outperforming traditional crisp-set approaches in all tumor subregions.
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