基于令牌路由的解剖学特权蒸馏用于基于MRI的神经周围侵犯预测
Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion
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中文总结 AI 辅助
针对肝内胆管癌PNI预测，提出解剖学特权师生框架，训练时教师用带掩码MRI学习令牌路由，学生提炼指导，该方法在155名患者中表现良好，实现较高AUROC，且推理无需掩码，计算量和时间合理。
中文摘要 AI 辅助
神经周围侵犯（PNI）与肝内胆管癌术后不良预后相关，但需手术病理确诊。现有术前成像模型常依赖放射科医生定义的变量、增强成像或手动标注。我们提出一种用于从T2加权MRI进行患者水平PNI预测的解剖学特权师生框架。训练时，教师利用带有肿瘤和肝脏掩码的MRI学习密集令牌路由，学生提炼此指导以在固定预算下保留和聚合信息令牌。解剖学监督仅限于训练，部署模型推理时无需掩码。在155名患者中，该方法在相同协议下评估的仅MRI匹配基线中实现了最高平均AUROC为0.750，在Jetson Orin Nano超级开发者套件上每例有1.43 GFLOPs和8.02 ms。
英文摘要
Perineural invasion (PNI) is associated with poor postoperative outcomes in intrahepatic cholangiocarcinoma, but it is confirmed by surgical pathology. Existing preoperative imaging models often rely on radiologist-defined variables, contrast-enhanced imaging, or manual annotations. We propose an anatomy-privileged teacher--student framework for patient-level PNI prediction from T2-weighted MRI. During training, the teacher uses MRI with tumor and liver masks to learn dense token routing, and the student distills this guidance to retain and aggregate informative tokens under a fixed budget. Anatomical supervision is restricted to training, and the deployed model does not require masks at inference. In 155 patients, the proposed method achieved the highest mean AUROC of 0.750 among matched MRI-only baselines evaluated under the same protocol, with 1.43 GFLOPs and 8.02 ms per case on a Jetson Orin Nano Super Developer Kit.
