arXiv:2607. 06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 09240v1 Announce Type: cross Abstract: Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally.
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.
arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv:2607. 09884v1 Announce Type: cross Abstract: Multimodal medical models often degrade when inputs are missing, a common scenario in real-world clinical workflows.
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.