6-10 October 2024 • MARRAKESH / MOROCCO




Unlock Medical Machine Learning with Open Data

7 October 2024
Diamant Room, Palmeraie Convention Centre - Marrakesh, Morocco


Dear Researchers,

We are delighted to announce the inauguration of the Open Data initiative and event at MICCAI 2024.

As the landscape of medical machine learning continues to evolve, access to diverse, representative, and inclusive datasets has become paramount for addressing global healthcare challenges effectively. The Open Data initiative aims to empower collaboration and innovation by facilitating and promoting sharing of medical imaging datasets.

In its dawn, while open to any participants, the Open Data initiative focuses particularly on underrepresented populations and diseases. Despite significant progress in medical imaging research and the availability of several public datasets, there remains a critical gap in the availability of diverse datasets, such as from the African continent. By highlighting datasets from these currently underrepresented populations, we strive to address disparities in healthcare and promote inclusivity in machine learning research.

Therefore, our Open Data initiative has established the AFRICAI repository, with the aim of hosting publicly available high-quality medical imaging datasets, focused on underrepresented populations and diseases. It is closely related to the principles of FAIR data and Data-centric AI, where data and its systematic engineering are at the forefront of model development. After all, there are no "good" models without "good" data.

MICCAI will be held on October 6-10, 2024, at the Marrakesh Palmeraie Convention Centre, Morocco, and the Open Data event will run in parallel to the main track on October 7, 13.30-18.00 at Diamant room opposite the main conference room. Hence if you are visiting MICCAI, you can visit the Open Data event: no separate registration is required.

The presented data and related works will be selected based on peer-reviewed paper submissions. The MELBA journal will serve as the official journal for submissions from the MICCAI Open Data track.

We look forward to seeing you at the 1st Open Data session at MICCAI 2024 - welcome!


The main focus is on:

  • New medical imaging datasets that encompass diverse demographics, ethnicities, and medical conditions. This year is especially focused on the African continent, with the release of the AFRICAI repository, 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.


We invite submissions of papers describing novel datasets - especially from the African continent and other underrepresented populations and diseases - including methodologies for the data collection and curation, and innovative approaches for utilizing them in medical imaging research.

  • 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.
  • We very strongly motivate you to upload the data to the AFRICAI repository. For this, we are providing data hosting and guidelines, with structured upload and access maintenance protocols.
  • Adhere to its guidelines to ensure FAIR data.
  • Any associated code should be open source.
  • Documentation on what the dataset contains, how the data points were collected (and possibly selected and curated), their intended use including ethical and accountability considerations.
  • While submitting an abstract is not required (see below), we would greatly appreciate it so we can better estimate the review load.


Call for papers
Expected May 2024
Abstract submission deadline
23:59 CET, June 24, 2024
Paper submission deadline
23:59 CET, July 1, 2024
Review deadline
23:59 CET, July 15, 2024
Notification of acceptance
23:59 CET, July 19, 2024
Camera-ready paper submission
23:59 CET, August 19, 2024


The AFRICAI repository will be hosted at the Euro-BioImaging Medical Imaging Archive XNAT. Details on the data model and how to upload your data in the repository can be found here.



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, Medical machine learning
Dept. of Oncology-Pathology, Karolinska Institutet, Sweden