Medical Image Computing in Resource Constrained Settings Workshop & KI (MIRASOL)

Welcome

Over recent years, there has been considerable excitement about the extraordinary opportunities that machine learning (ML) may offer in the healthcare of tomorrow. Given the potential of ML technology in facilitating the quantification of large and complex datasets, medical imaging has witnessed rapid and revolutionary developments. However, a limitation of current ML developments for medical imaging is that they have overwhelmingly and almost entirely targeted imaging applications in resource-rich settings. Hence, it is important to promote and accelerate the development of trustworthy and accessible ML solutions for medical imaging in resource-constrained settings (RCS), particularly, low-to-middle-income countries (LMICs) to advance global healthcare.

Aim:

This workshop aims to connect researchers, medical experts, policymakers, regulators, and industry partners from around the world who are working on translational ML solutions for real-world medical imaging in resource-constrained settings (RCS). This workshop builds on the successful MICCAI Meets Africa Workshop, organized in 2024 for the Morrocco meeting. While the 2024 workshop focused on ML solutions for Africa-specific healthcare challenges, this year’s workshop will broaden the scope to medical imaging challenges in countries and clinical practices with limited infrastructure, personnel, and funding resources.

Research and ML methods on optimization of imaging systems (e.g., accelerated imaging, low-resolution image enhancement, denoising, missing sequences), improvement in human resources (e.g., rapid diagnosis, reporting), and policy efforts for translation of ML from concept to clinic in RCS will be highlighted. The workshop will foster ML imaging innovations that address the general challenge of fewer datasets in RCS and inspire the wider MICCAI community to develop methods in a sustainability conscious and cost-effective manner (do more with little).

Workshop Focus Area

We are looking for long and short papers that will be presented as contributed talks. Specifically, we encourage contributions that highlight issues related to:

Theoretical and practical advances in machine learning algorithms and methods for medical imaging tailored for monitoring disease progression in settings with data scarcity, imbalanced representations, and limited computational resources.
Clinical outcome prediction and forecasting methods in multimodal healthcare data.
Societal and policy impacts of healthcare ML solutions in the global South focused on insights obtained via pilot studies, qualitative research, and human-in-the-loop settings.
Approaches to build and rapidly scale capacity for ML innovations in health in RCS, particularly in LMICs.

Call for Papers: Coming Soon

Important Dates

Full Paper Submission Deadline: June 25th, 2025 11:59 PM PST

Notification of Acceptance: July 23rd, 2025

Camera-ready Submission: August 23rd 2025, 11:59 PM PST

Workshop Date: September 23 or 27th 2025

Workshop Proceedings Submission: October 23rd, 2025

Registration:

To register to attend, visit the MICCAI 2025 conference registration page to complete your registration. All registered attendees of the MICCAI 2025 Conference are welcome to participate in this workshop.

Please note that this workshop is a satellite event of the MICCAI 2025 conference and an additional registration fee is required to attend MICCAI satellite events, including this workshop. See the MICCAI 2025 Conference Registration page for more details.

Organizers

Udunna Anazodo, PhD

Montreal Neurological Institute (MNI), McGill University, Montreal, Canada & Medical Artificial Intelligence (MAI) Lab, Lagos, Nigeria
Karim Lekadir, PhD

Department of Mathematics and Computer Science , Universitat de Barcelona
Celia Cintas, PhD

IBM Research Africa - Nairobi
Tinashe Mutsvangwa, PhD

University of Cape Town, South Africa & IMT-Atlantique, France
Alessandro Crimi, PhD

AGH University of Krakow, Poland
Farouk Dako, MD, MPH

Dept. of Radiology, University of Pennsylvania