29th INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING
AND COMPUTER ASSISTED INTERVENTION
4-8 OCTOBER 2026ADNEC CENTRE/ABU DHABI

OPEN DATA

OPEN DATA 2026

OPEN DATA MICCAI 2026

Unlock Medical Machine Learning with Open Data

ADNEC CENTRE - ABU DHABI, UAE


WELCOME MESSAGE

Dear Researchers,

We are pleased to announce the third edition of Open Data at MICCAI 2026.

As medical machine learning continues to advance, access to diverse, representative, and inclusive datasets remains a key bottleneck for addressing global healthcare challenges. The Open Data initiative aims to foster collaboration and innovation by encouraging the sharing of high-quality medical imaging datasets within the MICCAI community.

Building on the success of the first two editions, Open Data at MICCAI 2026 continues to place a strong emphasis on underrepresented populations and diseases. Despite substantial progress in medical imaging research and the growing availability of public datasets, significant gaps persist, particularly for regions such as the Middle East. By showcasing datasets that reflect underrepresented populations and clinical conditions, this initiative seeks to reduce disparities in healthcare research and promote more equitable and inclusive machine learning development.

As in previous years, we are creating the space and community that supports and provides more publicly available, high-quality medical imaging datasets, with a particular focus on underrepresented populations and diseases. Details regarding the submission process and storage guidelines will be shared at a later stage. The initiative remains closely aligned with the principles of FAIR data and Data-centric AI, emphasizing the critical role of well-curated data in building reliable and generalizable models. After all, there are no good models without good data.

New for 2026, thanks to the MICCAI Health Equity Grant, we are pleased to introduce micro-grants for dataset providers. These micro-grants aim to support researchers and institutions in preparing, curating, and sharing high-impact datasets, particularly those representing underrepresented populations and regions. Further details regarding eligibility, application procedures, and grant amounts are provided below.

MICCAI 2026 will take place from October 04 - 08, 2026, at the ADNEC Centre, Abu Dhabi, UAE. The Open Data event will run in parallel with the main conference. If you are attending MICCAI, you are welcome to visit the Open Data sessions, and no separate registration is required.

Datasets and related works will be selected through a peer-reviewed paper submission process. The MELBA journal will serve as the official journal for submissions from the MICCAI Open Data track.

We look forward to welcoming you to the third Open Data session at MICCAI 2026 and to continuing our collective efforts toward more inclusive and equitable medical AI research.


OPEN DATA MICROGRANTS

We are pleased to announce Open Data Micro-Grants as part of the Health Equity Award.

These grants support the creation of original, publicly available open datasets in medical imaging and clinical AI, with a focus on health equity, diversity, and under-represented populations.

What we fund
  • Data preparation and curation
  • Annotations and quality control
  • Documentation and public data release

Travel costs are not eligible.

Funding
  • 4 x Small grants (USD 650)
  • 4 x Large grants (USD 1,200)
Who can apply
  • Researchers from academia, hospitals, or industry
  • Individuals or teams
  • No geographic or career-stage restrictions
Key requirements
  • Public release with an open license
  • Original datasets (extensions of public datasets allowed)
  • Ethical approval already obtained
  • Funding released after paper submission (acceptance not required)
Timeline

Applications are closed

  • Proposal submission: February 22nd, 2026, 23.59 CET
  • Decision: March 01, 2026, 23.59 CET

PLEASE CLICK HERE TO VIEW OPEN DATA MICRO-GRANTS 2026 RESULTS


REPOSITORY

Guidelines on data submission, including upload procedures and storage details, will be provided in due course.


TOPICS OF INTEREST

The main focus is on:

  • New medical imaging datasets that encompass diverse demographics, ethnicities, and medical conditions. This year is especially focused on the Middle East, but we also welcome datasets from other (underrepresented) populations or diseases.
  • Updated or re-designed datasets based on previously publicly available data.

Additional topics include:

  • Dataset collection and annotation techniques.
  • Data augmentation strategies for improving dataset diversity.
  • Ethical considerations in data sharing and privacy preservation.
  • Applications of open data in medical image analysis, diagnosis, and treatment planning.
  • Challenges and opportunities in accessing and utilizing underrepresented datasets.

IMPORTANT DATES

Call for papers
Expected May 2026
Paper submission deadline
June 19, 2026, 23:59 CET
Review deadline
July 10, 2026, 23:59 CET
Notification of acceptance
July 20, 2026, 23:59 CET
Camera-ready paper submission
August 2, 2026, 23:59 CET

SUBMISSION

We welcome submissions of papers presenting novel datasets, particularly those from the Middle East and other underrepresented populations and diseases - including methodologies for the data collection and curation, and innovative approaches for utilizing them in medical imaging research.

THE PAPER SUBMISSION SITE WILL BE ANNOUNCED HERE AND OPEN IN MAY.

SCOPE
  • Encourage and empower through an open repository the sharing and dissemination of open-access datasets to facilitate collaboration and reproducibility.
  • Promote awareness and understanding of the importance of inclusivity and representative data in developing robust and equitable healthcare solutions.
  • Facilitate networking opportunities among researchers, data custodians, and stakeholders interested in leveraging open data for medical machine learning.
CRITERIA
  • Guidelines on data submission, including upload procedures and storage details, will be provided at a later stage.
  • Adhere to the FAIR data guidelines.
  • Any associated code should be open source.
AUTHOR GUIDELINES

We invite authors to carefully review our updated author guidelines document:

PLEASE CLICK HERE TO VIEW OPEN DATA MICCAI 2026 AUTHOR GUIDELINES

New this year: to improve our review process, we now require the authors to provide the link to their repository upon submission. If the dataset is going to be shared with restricted access, the authors are invited to provide a private link or create a reviewer account for the Open Data 2026 reviewers to be able to access and review the repository.

For more information on licensing and ethics, please read the information in the guide below:

DATA, LICENSING & ETHICS 101


REVIEW GUIDELINES

This is a single-blind review process. For general guidelines on "What Makes a Good Review" please refer to the corresponding section in the MICCAI reviewer guidelines.

For this track, pay special attention on the Submission guidelines listed above - scope and criteria, summarized below (in order of priority):

  1. Data availability and adherence to the FAIR data principles.
  2. Licensing, potential use cases, and ethical considerations/approvals.
  3. Methods used for the data and meta-data creation/collection: from the methods and equipment used for the acquisition to final processing, and the AI-ready state of the dataset (cleaning, curation, possible suggested splits, etc.).
  4. Clarity of the dataset specifics.
  5. Dataset usage showcase(s) with evaluation results.

ORGANIZING COMMITTEE

Martijn P.A. Starmans, PhD
Assistant Professor AI for Integrated Diagnostics (AIID)
Dept. of Radiology & Nuclear Medicine, Dept. of Pathology
Erasmus University Medical Center, Rotterdam, the Netherlands
Apostolia Tsirikoglou, PhD
Research Specialist, AI for Breast Imaging
Dept. of Oncology-Pathology,
Karolinska Institutet, Sweden
Lidia Garrucho Moras, PhD
Postdoc in AI for Medical Imaging
Artificial Intelligence in Medicine Lab
University of Barcelona, Barcelona, Spain
Kaouther Mouheb
PhD Candidate, Medical Image Analysis and Federated Learning
Dept. of Radiology & Nuclear Medicine
Erasmus University Medical Center, Rotterdam, the Netherlands
Laura Arbelaez Ossa, MD, PhD
AI Ethicist, Implementation and regulatory affairs for Digital Health
Artificial Intelligence in Medicine Lab
University of Barcelona, Barcelona, Spain