mlsanityData to 7 Oct 2026

Hierarchical MoE for Multi-Modal ILD Diagnosis

arXiv 2608.25261 ↗First posted 26 Aug 2026cs.AI cs.CVcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 11 of its 22 references that are in the map. First posted on arXiv 26 Aug 2026.

Builds on

11 of 22 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis6 Oct

Other references · 10

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  3. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  5. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  6. 2021Scaling Vision with Sparse Mixture of Expertscited by 740
  7. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  8. 2019Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentationcited by 40
  9. 2024A review of deep learning-based information fusion techniques for multimodal medical image classificationcited by 25
  10. 2022Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Datacited by 18

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