Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion

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[Submitted on 13 Jul 2026]

Title:Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion

View a PDF of the paper titled Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion, by Hyunsu Go and 17 other authors

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Abstract: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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Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.11987 [cs.CV]

(or arXiv:2607.11987v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.11987

arXiv-issued DOI via DataCite

Submission history

From: Dohyun Kweon [view email] [v1] Mon, 13 Jul 2026 14:18:22 UTC (1,407 KB)

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