OPEN DATA MICCAI 2024
Unlock Medical Machine Learning with Open Data
7 October 2024
Diamant Room, Palmeraie Convention Centre - Marrakesh, Morocco
WELCOME MESSAGE
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!
AFRICAI REPOSITORY
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 in our AFRICAI repository white paper here.
SHARE DATA
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 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.
IMPORTANT DATES
- Paper submission deadline
- 23:59 CET, July 15, 2024
- Review deadline
- 23:59 CET, July 30, 2024
- Notification of acceptance
- 23:59 CET, August 5, 2024
- Camera-ready paper submission
- 23:59 CET, September 5, 2024
SUBMISSION
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.
PAPER SUBMISSION SITE
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
- You must
upload the data to the AFRICAI repository. For this, we are providing data hosting and guidelines, with structured upload and access maintenance protocols. Please use the AFRICAI repository data submission form, and contact us for special requests. Upon submission the data does not have to be uploaded yet, only the submission form has to be submitted. Before acceptance, the data has to be uploaded. If you have strong reasons why this is not possible for your dataset, please contact the Open Data Chairs to see if an exception can be made. Technical details on data submission can be found in our AFRICAI repository white paper.
- Adhere to its guidelines to ensure FAIR data.
- 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 2024. 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 2024.
- 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 (i.e., melba-sample.tex).
- NO cover letter addressed to MELBA is needed at this stage.
- Please follow the format of MELBA Resource manuscripts for Data Resources, available here.
- 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.
- 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.
- A print of the AFRICAI repository data submission form filled specifically for the presented data.
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):
- Data availability and adherence to the FAIR data principles.
- Licensing, potential use cases, and ethical considerations/approvals.
- 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.).
- Clarity of the dataset specifics.
- 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, Medical machine learning
Dept. of Oncology-Pathology, Karolinska Institutet, Sweden
