用教师-学生框架解决脑肿瘤分割标注数据少的问题。
Addressing data annotation scarcity in Brain Tumor Segmentation on 3D MRI scan Using a Semi-Supervised Teacher-Student Framework
- 教师生成带置信度的伪标签，学生按信心分阶段学习
- 仅用10%标注数据时，DSC达0.872，早期提升显著
- 能修复教师遗漏的增强型病灶，适合标注稀缺场景
从MRI中精准分割脑肿瘤受限于昂贵的标注和跨扫描仪、站点的数据异质性。我们提出一种半监督教师-学生框架，结合不确定性感知的伪标签教师与渐进式置信度课程。教师生成概率掩码和像素级不确定性；未标注扫描按图像级置信度排序，分阶段引入，双损失目标使学生学会高置信区域并遗忘低置信区域。基于一致性的精炼进一步提升伪标签质量。在BraTS 2021上，验证DSC从10%数据下的0.393提升至100%时的0.872，早期增益最大，体现数据效率。教师验证DSC达0.922，学生在肿瘤亚区表现更优（如NCR/NET 0.797，水肿0.980）；尤其恢复了教师失败的增强类（DSC 0.620）。结果表明，置信度驱动的课程与选择性遗忘可在有限监督和噪声伪标签下提供稳健分割。
原文摘要 · Abstract (English)
Accurate brain tumor segmentation from MRI is limited by expensive annotations and data heterogeneity across scanners and sites. We propose a semi-supervised teacher-student framework that combines an uncertainty-aware pseudo-labeling teacher with a progressive, confidence-based curriculum for the student. The teacher produces probabilistic masks and per-pixel uncertainty; unlabeled scans are ranked by image-level confidence and introduced in stages, while a dual-loss objective trains the student to learn from high-confidence regions and unlearn low-confidence ones. Agreement-based refinement further improves pseudo-label quality. On BraTS 2021, validation DSC increased from 0.393 (10% data) to 0.872 (100%), with the largest gains in early stages, demonstrating data efficiency. The teacher reached a validation DSC of 0.922, and the student surpassed the teacher on tumor subregions (e.g., NCR/NET 0.797 and Edema 0.980); notably, the student recovered the Enhancing class (DSC 0.620) where the teacher failed. These results show that confidence-driven curricula and selective unlearning provide robust segmentation under limited supervision and noisy pseudo-labels.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。