发表机构
Babeș-Bolyai University（巴比什-波雅依大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对术后胶质瘤分割难题，通过脑掩码百分位数归一化与体素级对比学习结合，及提出子空间感知类注意力模块改进分割效果，经nnU-Net整合后在相关数据集上取得更好的分割结果。
AI 中文摘要
术后追踪残留肿瘤对早期发现复发至关重要，但自动化术后胶质瘤分割仍是难题。基于Transformer的架构虽有成果，但跨临床方案的泛化性研究少。本文在相关数据集上进行消融研究，发现标准广义骰子损失在域转移下不稳定。为此，将脑掩码百分位数归一化与体素级对比学习结合，还提出子空间感知类注意力模块，用nnU-Net整合改进后，实现了更好的结果。
英文摘要
Tracking residual tumor after surgery is essential for catching recurrence early, but automating post-operative glioma segmentation remains a difficult task. Although transformer-based architectures, such as SwinUNETR, achieved impressive results, few studies test how well they generalize across clinical protocols. In this paper, we conduct an ablation study on the MU-GLIOMA-POST and UCSF-ALPTDG datasets and show that the standard Generalized Dice Loss (GDL) is unstable under domain shift: the Whole Lesion (WL) Dice drops from 0.88 on the internal validation set to 0.73 on the external UCSF test set. To address this, we pair brain-masked percentile normalization with voxel-level contrastive learning. We also propose a Subspace-Aware Class Attention (SACA) module that re-calibrates the bottleneck features and raises Enhancing Tumor (ET) sensitivity by 8% (9.1% relative improvement) on internal validation. Ensembling these refinements with nnU-Net brings every stable configuration to a WL Dice of 0.94, and the SACA variant ensemble achieves the best boundary error (HD95) of 2.92 mm on MU-GLIOMA-POST.
CommentsAccepted for presentation at the International Conference on Artificial Neural Networks (ICANN 2026)
