用术后文本知识提升术前脑瘤预后预测，无需推理时提供文本。
HyperPriv-EPN: Hypergraph Learning with Privileged Knowledge for Ependymoma Prognosis
- 构建双流超图框架，用术后的文本信息训练模型学习语义结构。
- 在311例多中心数据上实现最优诊断准确率与生存分层效果。
- 适合需要利用历史病历提升术前诊断的医疗AI研究者使用。
Ependymoma术前预后对治疗方案制定至关重要，但因术前MRI缺乏术后病理报告中的语义信息而困难重重。现有跨模态方法无法在推理阶段使用这些特权文本数据。为此，我们提出基于超图学习的特权信息利用框架HyperPriv-EPN。采用分割图策略，通过共享编码器分别处理包含术后特权信息的教师图和仅含术前影像的学生图。通过双流蒸馏，学生图仅凭视觉特征即可“幻化”出语义社区结构。在包含311名患者的多中心队列上验证，HyperPriv-EPN实现了当前最优的诊断准确率与生存分层能力。该方法有效将专家知识迁移至术前场景，使历史术后数据可指导新患者诊断，且无需在推理时提供文本。
原文摘要 · Abstract (English)
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.
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