用解剖先验指导MRI分析，提升肝内胆管癌神经侵犯预测准确率。
Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion
- 基于解剖掩码训练教师模型，学习密集令牌路由策略。
- 学生模型在有限计算预算下高效保留关键信息，平均AUROC达0.750。
- 推理无需掩码，适合临床部署，单例仅需8.02毫秒。
肝内胆管癌的神经侵犯（PNI）与术后不良预后相关，但确诊依赖手术病理。现有术前影像模型多依赖放射科医生定义变量、增强成像或人工标注。本文提出一种解剖先验引导的师生框架，仅用T2加权MRI实现患者级PNI预测。训练时，教师模型利用肿瘤和肝脏掩码学习密集令牌路由；学生模型则在固定计算预算下蒸馏该引导，保留并聚合有用信息。解剖监督仅用于训练，部署模型推理时不需掩码。在155名患者上，该方法在相同协议下优于所有匹配的MRI-only基线，平均AUROC达0.750，单例耗时8.02毫秒，计算量为1.43 GFLOPs（Jetson Orin Nano Super开发者套件）。
原文摘要 · Abstract (English)
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.
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