ReMoE：报告引导型混合专家模型用于多模态OCT/OCTA异常检测
ReMoE: Report-Guided Mixture-of-Experts for Multimodal OCT/OCTA Anomaly Detection
AI总结：
针对多模态OCT/OCTA异常检测，提出ReMoE模型，融合正常报告语义构建模态感知先验，在两个数据集上取得最优性能。
AI中文摘要：
多模态医学异常检测旨在识别偏离正常模式的样本，由于异常样本稀缺，基于正常数据构建正常性模型是可行的。在视网膜光学相干断层扫描（OCT）和OCT血管造影（OCTA）异常检测中，现有无监督方法依赖视觉特征分布、重建残差或编码器-解码器差异，使异常得分依赖外观层面偏差，而多模态正常性还包含正常医学报告描述的语义组织。为此，我们提出报告引导型混合专家模型（ReMoE），其将正常报告语义提炼为图像-文本先验学生模型，构建模态感知先验，并使用报告引导型模态调制（RMM）通过混合专家路由调制特征。在含配对正常报告的私有OCT/OCTA数据集及使用固定正常报告的公开OCTA500-3MM设置上的实验，证明了其达到当前最优性能。
英文摘要：
Multimodal medical anomaly detection identifies samples deviating from normal patterns, where scarce abnormal cases make normality modeling from normal data practical. In retinal Optical Coherence Tomography (OCT) and OCT Angiography (OCTA) anomaly detection, existing unsupervised methods rely on visual feature distributions, reconstruction residuals, or encoder-decoder discrepancies, making anomaly scores depend on appearance-level deviations, while multimodal normality also contains semantic organization described in normal medical reports. To this end, we propose Report-Guided Mixture-of-Experts (ReMoE), which distills normal report semantics into an image-to-text prior student, builds modality-aware priors, and uses Report-Guided Modality Modulation (RMM) to modulate features through mixture-of-experts routing. Experiments on a private OCT/OCTA dataset with paired normal reports and a public OCTA500-3MM setting using a fixed normal report demonstrate state-of-the-art performance.
