用结构共识提升胰腺分割的少样本泛化能力
SCKAN: Structural Consensus-based KAN Prototype Learning for Semi-Supervised Pancreas Segmentation
- 通过原型对比优化实现跨样本结构一致性
- 在两个公开数据集上达到新高准确率
- 适合少标注医学图像分割场景
精准胰腺分割对早期癌症诊断至关重要，但标注稀缺迫使采用半监督学习（SSL）。然而，由于样本间形态差异大，现有方法在稀疏标注下泛化能力差，产生监督偏差。为此，提出结构共识型KAN原型学习（SCKAN），首次引入基于科尔莫戈罗夫-阿诺尔德网络（KAN）的跨样本结构共识学习，实现更鲁棒的分割。核心设计包括：结构约束原型一致性学习（SPCL），通过原型级对比优化强制跨样本结构一致性；基于共识的科尔莫戈罗夫-阿诺尔德融合（CKaF），利用KAN的自适应B样条非线性聚合稳定共识并过滤样本噪声。在两个公开胰腺数据集上的大量实验验证了SCKAN的有效性。代码已开源。
原文摘要 · Abstract (English)
Accurate pancreas segmentation is critical for early cancer diagnosis, where annotation scarcity necessitates Semi-Supervised Learning (SSL). However, due to significant inter-sample morphological variability, existing SSL methods face severe generalizability limitations under sparse supervision, leading to the Supervision Bias problem. To address this, we propose Structural Consensus-based KAN Prototype Learning (SCKAN), which constructs the first cross-sample structural consensus learning with Kolmogorov-Arnold Networks (KANs), to achieve more generalizable and accurate segmentation. Specifically, SCKAN contains two key designs: Structure-constrained Prototype Consistency Learning (SPCL), which prompts unbiased structural representation by enforcing cross-sample consistency via prototype-level contrastive optimization, and Consensus-based Kolmogorov-Arnold Fusion (CKaF), which reduces morphology-specific bias by aggregating stable consensus and filtering sample-wise noise via KAN's adaptive B-spline nonlinearity. Extensive experiments on two public pancreas datasets demonstrate the effectiveness of SCKAN. Code is at https://github.com/rhodaliu17/SCKAN.
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