Hierarchical MoE for Multi-Modal ILD Diagnosis

• URL: http://arxiv.org/abs/2608.25261v1
• Date: Wed, 26 Aug 2026 00:53:55 GMT
• Title: Hierarchical MoE for Multi-Modal ILD Diagnosis
• Abstract summary: We present a hierarchical multimodal MoE for interstitial lung disease (ILD) classification.<n>A gate assigns patient-specific weights to imaging and EHR predictions.<n>A sub-gating module decomposes the EHR branch into clinically defined feature groups.
• Score: 33.57244100206671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture-of-experts (MoE) models combine specialized predictors under learned routing, offering a principled mechanism for leveraging heterogeneity in medical data. We present a hierarchical multimodal MoE for interstitial lung disease (ILD) classification that integrates a frozen, pre-trained imaging expert with structured electronic health records (EHR) via two-stage gating. A modality-level gate assigns patient-specific weights to imaging and EHR predictions, while a sub-gating module decomposes the EHR branch into clinically defined feature groups with learned, group-specific contributions. This design preserves stable imaging representations while enabling input-dependent clinical weighting and explicit EHR specialization. Under strict patient-level cross-validation, the model achieved the highest mean AUC among the evaluated methods (0.8750 +- 0.0443), compared with 0.8646 for imaging-only REN and 0.7685 for SwinUNETR. The framework extends interpretability across anatomical regions, imaging--EHR utilization, and clinically defined EHR feature groups.

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