MICCAI 2026

MultiTab 2026

MICCAI Workshop on Multimodal Learning with Medical Tabular Data

In conjunction with the 29th International Conference on Medical Image Computing and Computer Assisted Intervention

About the Workshop

Tabular clinical data—including electronic health records (EHRs), laboratory measurements, omics data, and other clinical variables—remain underutilized in medical imaging research despite their widespread availability in biobanks and clinical repositories. Their heterogeneous structure, variable coding schemes, and substantial missingness make them difficult to model and integrate with imaging and other modalities.

MultiTab focuses on advancing methodological approaches that address these challenges, positioning tabular and semi-structured medical data as a core component that can enhance medical imaging analysis. We aim to gather researchers developing AI methods for learning from diverse tabular data types and for combining them with image data and other complementary modalities.

Part of "Multimodal Learning" Theme

MultiTab 2026 is part of the "Multimodal Learning" theme at MICCAI 2026.

Related workshops:

SE 1: ML-CDS
Multimodal Learning for Clinical Decision Support

SE 2: MULTITAB (W)
2-hour workshop on multimodal tabular data

Call for Papers

We welcome contributions across the following topics:

Fusion of Imaging, Tabular, and Multimodal Data

Novel architectures and techniques for integrating imaging with tabular data (e.g., clinical, omics, EHRs) and other modalities (e.g., text, time-series, signals).

Multimodal Inference Challenges

Generalization, robustness, and trustworthiness in models combining tabular and imaging data with focus on scalability, interpretability, and uncertainty quantification.

Foundation Models for Multimodal Learning

Adaptation, fine-tuning, and evaluation of foundation models to enhance fusion and representation learning across tabular and imaging data.

Trustworthy and Explainable AI

Cross-modal interpretability, fairness, bias mitigation, and compliance in models leveraging both imaging and structured tabular data.

Benchmarking and Reproducibility

Standards, datasets, and evaluation metrics specifically designed for multimodal research involving imaging and tabular data.

Handling Data Heterogeneity

Techniques for missing data, noise, multi-source integration, domain shifts, and longitudinal multimodal analysis with temporal alignment.

Submission Information

Submission Portal

OpenReview

Submit your paper through OpenReview

Submission Guidelines

Follow MICCAI Standards

We follow the MICCAI main conference submission guidelines for paper format, length, and quality standards to ensure consistency across all MICCAI 2026 events.

Double-blind review
8 pages + references
LNCS format

MICCAI Guidelines

Important Dates

Submission

July 1, 2026

Paper Submission Deadline

Submit your full paper through OpenReview

July 31, 2026

Acceptance Notifications

Authors notified of acceptance/rejection decisions

October 6, 2026

Workshop Date

2-hour workshop at MICCAI 2026

Program

Coming Soon

The detailed program schedule will be finalized and announced soon. The workshop will take place on October 6, 2026 and will be 2 hours long at MICCAI 2026.

Please check back for updates on keynote speakers, scientific talks, and poster sessions.

Keynote Speakers

Coming Soon

Keynote speakers will be announced soon

Workshop Organizers

Maxime Di Folco

Maxime Di Folco

Associate Professor

Télécom Paris

Website

Chen Qin

Chen Qin

Associate Professor

Imperial College London

Website

Laura Daza

Laura Daza

Post-doctoral Researcher

Helmholtz Munich & TUM

Website

Nikola Simidjievski

Nikola Simidjievski

Associate Professor

Télécom Paris

Website

Julia Schnabel

Julia Schnabel

Professor

Helmholtz Munich & TUM

Website

Marta Hasny

Marta Hasny

PhD Researcher

Helmholtz Munich & TUM

Website

Jun Li

Jun Li

PhD Researcher

Technical University of Munich

Website

Siyi Du

Siyi Du

PhD Researcher

Imperial College London

Website

Organized By

Helmholtz Munich
TUM
Imperial College London
Télécom Paris
IP Paris