Full paper
8-page papers in LNCS format following MICCAI guidelines, submitted via OpenReview. Supplementary material is not supported; reproducibility resources should be shared via public code repositories.
Machine Learning/CORE Unranked
MultiTab 2026
Multimodal Learning with Medical Tabular Data
MultiTab 2026 is a MICCAI workshop focused on advancing multimodal learning techniques that integrate medical tabular data (such as EHRs, lab results, and omics) with medical imaging and other modalities. The workshop aims to address challenges like data heterogeneity, missingness, and model interpretability to enhance the clinical utility of combined data sources in medical AI.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Research areas in scope
01
Topics
Fusion of Imaging, Tabular, and Multimodal DataMultimodal Inference ChallengesFoundation Models for Multimodal LearningTrustworthy and Explainable AIBenchmarking and ReproducibilityHandling Data Heterogeneity
Policies worth checking twice
- Double-blind review
- 8-page limit including references
- Must follow MICCAI submission guidelines for format and quality
- Supplementary material submissions are not supported
- All essential information for review must be in the main paper
- Authors must provide public code repositories for reproducibility if needed
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026