arXiv Computer Vision
DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis
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arXiv AI
Jun 15
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
arXiv:2512. 10966v3 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data.
arXiv Machine Learning
Jul 31
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
arXiv Machine Learning
Oct 9
CoPoE: Multimodal Fusion via Decomposable Disease-Coordinate Product-of-Experts for Missing-Modality Alzheimer's Diagnosis
CoPoE is a new multimodal fusion framework for Alzheimer’s disease diagnosis that maps diverse clinical, imaging, genomic, and biomarker data into a structured latent space divided into four biological and clinical axes: genetic Risk, molecular Pathology, Neurodegeneration, and clinical Stage. Each available modality contributes a diagonal Gaussian expert, and a masked Product-of-Experts architecture fuses only the observed modalities, avoiding synthetic imputation. In extensive experiments on the ADNI dataset, CoPoE outperforms standard missing-modality fusion baselines, achieving higher AUROC, better calibration metrics, and retaining pathology signals even when certain modalities are missing.
arXiv AI
Jun 9
NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
arXiv:2606. 07635v1 Announce Type: cross Abstract: Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations.
arXiv AI
Sep 12
Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding
The paper introduces CoMA-DiT, a bidirectional cross‑modal Diffusion Transformer that uses paired modalities as mutual generative supervision for latent augmentation rather than just inputs for fusion. By conditioning velocity prediction on the paired modality through cross‑modal attention and injecting variation via a reliability‑gated residual mechanism, CoMA‑DiT improves multimodal brain state decoding. Experiments on auditory attention decoding and emotion recognition show consistent gains over 20 baselines, with absolute accuracy and macro‑F1 improvements of 4.28% and 6.70% respectively, and extensive analyses confirm its robustness and interpretability.
arXiv AI
Jun 10
LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.