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: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.
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: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.
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:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.
arXiv:2607. 01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders.
The paper introduces Generalist‑Specialist Mixture‑of‑Experts (GS‑MoE), a two‑branch architecture that combines a cross‑modal generalist model with modality‑specific specialists through domain‑constrained feature fusion. GS‑MoE improves detection of rare pathologies in multimodal medical imaging, achieving significant per‑class F1 gains and outperforming dense and specialist‑only MoE baselines while using about 53% fewer active parameters at inference. The study demonstrates that balancing cross‑modal shared representations with expert routing can enhance performance on low‑prevalence conditions.
arXiv:2608. 13911v1 Announce Type: new Abstract: Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individual records within the same client may contain different partial modality subsets.
The survey titled "Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey" reviews how Mixture-of-Experts (MoE) frameworks address multimodal learning problems. It categorizes MoE’s contributions into three roles: an efficient multimodal engine that scales without excessive computation, a representation learner that fuses diverse expert knowledge, and an adapter that handles imperfect data such as missing modalities. The paper also highlights research gaps like interpretable routing, expert communication, and lifelong multimodal learning.
The paper introduces Neuro‑JEPA, a sparse multimodal foundation model that learns unified representations of brain MRI across T1w, T2w, and FLAIR sequences using a latent predictive objective and a Mixture‑of‑Experts architecture. It was pretrained on over 1.5 million scans from 428,647 studies and systematically evaluates architectural, masking, objective, and sparsity choices for robust multimodal representation learning. Across 47 tasks from three health systems and 12 public datasets, Neuro‑JEPA consistently outperforms a simple CNN baseline, demonstrating its effectiveness for both clinical and research applications.
arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.