提升医学图像病灶分割精度，尤其改善边界模糊与小病灶遗漏问题。
UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation
- 通过不确定性引导的超图精炼，增强模糊区域的分割能力。
- 在五个公开数据集上优于主流方法，小病灶分割效果显著提升。
- 适合医疗影像分析、病灶精准识别等临床应用研究者参考。
准确的病灶分割对临床诊断和治疗规划至关重要。然而，病灶常与周围组织相似且边界模糊，导致边界区域预测不稳定；同时，多尺度特征提取会稀释小病灶信号，引发分割不足或过度。为此，本文提出不确定性感知的超图精炼网络（UHR-Net）。首先，设计不确定性导向的实例对比预训练策略（UO-IC），结合几何感知的复制粘贴增强与病灶类背景区域的困难负样本挖掘，提升小病灶及视觉模糊病灶的实例区分能力。其次，提出不确定性引导的超图精炼（UGHR）模块，从粗略概率图生成基于熵的不确定性图，指导超图精炼过程。通过将超边原型分为前景与背景组，解耦高阶交互关系，增强在模糊区域的精炼效果。在五个公开基准数据集上的实验表明，UHR-Net consistently 超越强基线模型。代码已开源：https://github.com/CUGfreshman/UHR-Net。
原文摘要 · Abstract (English)
Accurate lesion segmentation is crucial for clinical diagnosis and treatment planning. However, lesions often resemble surrounding tissues and exhibit ill-defined boundaries, leading to unstable predictions in boundary/transition regions. Moreover, small-lesion cues can be diluted by multi-scale feature extraction, causing under- or over-segmentation. To address these challenges, we propose an Uncertainty-Aware Hypergraph Refinement Network (UHR-Net). First, we introduce an Uncertainty-Oriented Instance Contrastive (UO-IC) pretraining strategy that couples geometry-aware copy-paste augmentation with hard-negative mining of lesion-like background regions to improve instance-level discrimination for small and visually ambiguous lesions. Second, we design an Uncertainty-Guided Hypergraph Refinement (UGHR) block, which derives an entropy-based uncertainty map from a coarse probability map to guide hypergraph refinement. By splitting hyperedge prototypes into foreground and background groups, UGHR decouples higher-order interactions and improves refinement in ambiguous regions. Experiments on five public benchmarks demonstrate consistent gains over strong baselines. Code is available at: https://github.com/CUGfreshman/UHR-Net.
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