REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Authors:
Alec K. Peltekian,
Halil Ertugrul Aktas,
Gorkem Durak,
Kevin Grudzinski,
Bradford C. Bemiss,
Carrie Richardson,
Jane E. Dematte,
G. R. Scott Budinger,
Anthony J. Esposito,
Alexander Misharin,
Alok Choudhary,
Ankit Agrawal,
Ulas Bagci
Abstract:
Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing-assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease heterogeneity govern pathological patterns…
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Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing-assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease heterogeneity govern pathological patterns. We introduce Regional Expert Networks (REN), the first anatomically-informed MoE framework for medical image classification. REN encodes anatomical priors by training seven specialized experts, each dedicated to a distinct lung lobe or bilateral lung combination, enabling precise modeling of region-specific pathological variation. Multi-modal gating mechanisms dynamically integrate radiomics biomarkers with deep learning (DL) features extracted by convolutional (CNN), Transformer (ViT), and state-space (Mamba) architectures to weight expert contributions at inference. Applied to interstitial lung disease (ILD) classification on a 597-patient, 1,898-scan longitudinal cohort, REN achieves consistently superior performance: the radiomics-guided ensemble attains an average AUC of 0.8646 +- 0.0467, a +12.5 % improvement over the SwinUNETR single-model baseline (AUC 0.7685, p=0.031). Lower-lobe experts reach AUCs of 0.88-0.90, outperforming DL baselines (CNN: 0.76-0.79) and mirroring known patterns of basal ILD progression. Evaluated under rigorous patient-level cross-validation, REN demonstrates strong generalizability and clinical interpretability, establishing a scalable, anatomically-guided framework potentially extensible to other structured medical imaging tasks. Code is available on our GitHub https://github.com/NUBagciLab/MoE-REN.
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Submitted 30 March, 2026; v1 submitted 6 October, 2025;
originally announced October 2025.
Imaging-Based Mortality Prediction in Patients with Systemic Sclerosis
Authors:
Alec K. Peltekian,
Karolina Senkow,
Gorkem Durak,
Kevin M. Grudzinski,
Bradford C. Bemiss,
Jane E. Dematte,
Carrie Richardson,
Nikolay S. Markov,
Mary Carns,
Kathleen Aren,
Alexandra Soriano,
Matthew Dapas,
Harris Perlman,
Aaron Gundersheimer,
Kavitha C. Selvan,
John Varga,
Monique Hinchcliff,
Krishnan Warrior,
Catherine A. Gao,
Richard G. Wunderink,
GR Scott Budinger,
Alok N. Choudhary,
Anthony J. Esposito,
Alexander V. Misharin,
Ankit Agrawal
, et al. (1 additional authors not shown)
Abstract:
Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in systemic sclerosis (SSc). Chest computed tomography (CT) is the primary imaging modality for diagnosing and monitoring lung complications in SSc patients. However, its role in disease progression and mortality prediction has not yet been fully clarified. This study introduces a novel, large-scale longitudinal chest CT…
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Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in systemic sclerosis (SSc). Chest computed tomography (CT) is the primary imaging modality for diagnosing and monitoring lung complications in SSc patients. However, its role in disease progression and mortality prediction has not yet been fully clarified. This study introduces a novel, large-scale longitudinal chest CT analysis framework that utilizes radiomics and deep learning to predict mortality associated with lung complications of SSc. We collected and analyzed 2,125 CT scans from SSc patients enrolled in the Northwestern Scleroderma Registry, conducting mortality analyses at one, three, and five years using advanced imaging analysis techniques. Death labels were assigned based on recorded deaths over the one-, three-, and five-year intervals, confirmed by expert physicians. In our dataset, 181, 326, and 428 of the 2,125 CT scans were from patients who died within one, three, and five years, respectively. Using ResNet-18, DenseNet-121, and Swin Transformer we use pre-trained models, and fine-tuned on 2,125 images of SSc patients. Models achieved an AUC of 0.769, 0.801, 0.709 for predicting mortality within one-, three-, and five-years, respectively. Our findings highlight the potential of both radiomics and deep learning computational methods to improve early detection and risk assessment of SSc-related interstitial lung disease, marking a significant advancement in the literature.
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Submitted 27 September, 2025;
originally announced September 2025.