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HyperPriv-EPN: Hypergraph Learning with Privileged Knowledge for Ependymoma Prognosis

Shuren Gabriel Yu, Sikang Ren, Yongji Tian

TL;DR

HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework, introduces a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph and a Student graph, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.

Abstract

Preoperative prognosis of Ependymoma is critical for treatment planning but challenging due to the lack of semantic insights in MRI compared to post-operative surgical reports. Existing multimodal methods fail to leverage this privileged text data when it is unavailable during inference. To bridge this gap, we propose HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework. We introduce a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph (enriched with privileged post-surgery information) and a Student graph (restricted to pre-operation data). Through dual-stream distillation, the Student learns to hallucinate semantic community structures from visual features alone. Validated on a multi-center cohort of 311 patients, HyperPriv-EPN achieves state-of-the-art diagnostic accuracy and survival stratification. This effectively transfers expert knowledge to the preoperative setting, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.

HyperPriv-EPN: Hypergraph Learning with Privileged Knowledge for Ependymoma Prognosis

TL;DR

HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework, introduces a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph and a Student graph, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.

Abstract

Preoperative prognosis of Ependymoma is critical for treatment planning but challenging due to the lack of semantic insights in MRI compared to post-operative surgical reports. Existing multimodal methods fail to leverage this privileged text data when it is unavailable during inference. To bridge this gap, we propose HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework. We introduce a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph (enriched with privileged post-surgery information) and a Student graph (restricted to pre-operation data). Through dual-stream distillation, the Student learns to hallucinate semantic community structures from visual features alone. Validated on a multi-center cohort of 311 patients, HyperPriv-EPN achieves state-of-the-art diagnostic accuracy and survival stratification. This effectively transfers expert knowledge to the preoperative setting, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.
Paper Structure (24 sections, 5 equations, 2 figures, 2 tables)

This paper contains 24 sections, 5 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multimodal Survival Analysis and MIL
  4. Hypergraph Neural Networks (HGNN)
  5. Learning Using Privileged Information (LUPI)
  6. Method
  7. Problem Formulation and Framework Overview
  8. Hierarchical Feature Extraction
  9. Visual Encoding (3D MAE + HSSL)
  10. Privileged Text Encoding
  11. The Severed Graph Strategy
  12. Teacher Pass (Privileged Topology)
  13. Student Pass (Severed Topology)
  14. Feature Aggregation and Prediction
  15. Privileged Knowledge Distillation
  16. ...and 9 more sections

Figures (2)

  • Figure 1: The framework of HyperPriv-EPN. In Stage 1, we extract features from MRI scans, clinical data, and privileged surgery reports to construct multimodal nodes. Stage 2 involves a shared Hypergraph Neural Network encoder that processes both a Teacher hypergraph and a Student hypergraph concurrently. Finally, in Stage 3, node features are aggregated via gated attention pooling into patient-wise vectors, where feature distillation aligns the Student with the Teacher, followed by multi-task prediction heads for classification and regression that utilize logit distillation.
  • Figure 2: The KM curves of all the compared methods on prognosis metrics (PFS/OS). Predicted risk scores are split into high risk (red) and low risk (blue) based on the median grade.