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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MLMI_007.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=SYNHAiXICM
BibTex
@InProceedings{PelAle_Hierarchical_MICCAISAT2026,
author = { Peltekian, Alec K. AND Durak, Gorkem AND Aktas, Halil Ertugrul AND Richardson, Carrie Lynn AND Carns, Mary AND Aren, Kathleen AND Budinger, G. R. Scott AND Esposito, Anthony J. AND Misharin, Alexander AND Choudhary, Alok Nidhi AND Agrawal, Ankit AND Bagci, Ulas},
title = { { Hierarchical MoE for Multi-Modal ILD Diagnosis } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17272},
month = {pending},
page = {pending}
}