29th INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING
AND COMPUTER ASSISTED INTERVENTION
4-8 OCTOBER 2026 • ADNEC 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 open now

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

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
TBD
Review deadline
TBD
Notification of acceptance
TBD
Camera-ready paper submission
TBD

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

Listed below are important requirements, besides the above criteria, for preparing and submitting a manuscript to Open Data MICCAI 2026. Accepted papers will have the opportunity of oral presentation at the Open Data session, and will be invited for submission at the MELBA journal Resource track to be published in a special issue on Open Data MICCAI 2026.

  1. Manuscript template: Submissions must be limited to a maximum of 8 pages for text, figures, and tables and up to 2 additional pages for references. You must follow the MELBA journal latex template for submissions available here.

    1. NO cover letter addressed to MELBA is needed at this stage.
    2. Please follow the format of MELBA Resource manuscripts for Data Resources, available here.
    3. In addition to the above, we request that you include information on how the presented data adhere to the FAIR data principles; licensing, access procedures, and ethical considerations (including anonymization/pseudonymization practices); detailed descriptions of the (meta)data, including collection, equipment and acquisition protocols, data model and format, processing, curation, ground truth definition, and known errors/limitations; and comprehensive visual examples.

  2. Dataset description: To ensure the quality and utility of shared medical imaging datasets, we have established the following minimal information requirements for data submissions:

    1. Dataset Overview

      1. Title: A concise and descriptive title of the dataset.
      2. Abstract: A summary outlining the dataset's purpose, scope, and potential applications.

    2. Data Acquisition Details

      1. Imaging Modality: Specify the type of imaging used (e.g., CT, MRI, pathology, microscopy).

      2. Equipment Specifications

        1. Manufacturer and Model: Detail the imaging device's manufacturer, model, and any relevant technical characteristics (e.g. field strength for MRI)
        2. Acquisition Settings: Provide key parameters such as resolution, magnification, and imaging protocols.

    3. Subject Information

      1. Demographics: Include anonymized data on age, sex, and relevant clinical information.
      2. Cohort Description: Describe the selection criteria, including inclusion and exclusion parameters.

    4. Annotation and Segmentation

      1. Annotation Protocols: Detail the methods and standards used for annotations or segmentations, including any automatic tools used in the annotation process.
      2. Annotator Expertise: Specify the qualifications of individuals who performed the annotations.
      3. Inter-Observer Variability: If applicable, report measures of consistency among different annotators.

    5. Data Format and Structure

      1. File Formats: List the formats of the image and annotation files (e.g., DICOM, TIFF, JPEG).

  3. Supplementary materials: An upload link (of maximum two files) will be made available in your author console after you have created your submission. DO NOT append your supplementary material at the end of your main paper. As supplementary material you should upload any supporting information for the presented datasets that do not lie to the main manuscript requirements, e.g., additional visual examples.

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