October 6-10, 2024

MICCAI Meets Africa Workshop


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 high-income settings. Hence, it is important to promote and accelerate the development of trustworthy and accessible ML solutions for medical imaging in low-to-middle-income (LMIC) countries to advance global healthcare.



This workshop aims to connect researchers, medical experts, policymakers, and regulators from Africa and beyond to share experiences and initiatives in promoting ML for medical imaging on the African continent, by Africans for Africans. With this workshop, we hope to showcase exceptional research done on the continent, raise awareness about initiatives, attract collaborators, promote new research and innovation in the field to address Africa-specific healthcare challenges and encourage similar initiatives for promoting practical ML solutions for resource-limited settings around the world.

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

  1. 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.
  2. Clinical outcome prediction and forecasting methods in multimodal healthcare data.
  3. 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.
  4. Approaches to build and rapidly scale capacity for ML innovations in health in Africa.

See the Call for Papers for details of core focus areas and topics

Important Dates

Full Paper Submission Deadline: June 24th 2024, 11:59 PM EST

Notification of Acceptance: July 15th 2024

Camera-ready Submission: July 30th 2024, 11:59 PM PST

Workshop Date: October 6th 2024


To register to attend, visit the MICCAI 2024 conference registration page to complete your registration. All registered attendees of the MICCAI 2024 Conference are welcome to participate in this workshop. There is no additional cost to attend this workshop.


  • Dr. Celia Cintas

    Dr. Celia Cintas

    IBM Research Africa - Nairobi

    Dr. Celia Cintas is a Research Scientist at IBM Research Africa - Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on the improvement of ML techniques to address challenges on Global Health in developing countries and exploring subset scanning for anomaly detection under generative models. Previously, grantee from the National Scientific and Technical Research Council (CONICET) at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences, Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019), and Diversity Co-Chair for SciPy (2020-2022). Workshop Co-chair at ICLR 2023, Diversity Co-chair for ISBI-IEEE 2023-2024, among others. https://celiacintas.io/

  • Dr. Udunna Anazodo

    Dr. Udunna Anazodo

    Montreal Neurological Institute (MNI), McGill University, Montreal, Canada

    Dr. Udunna Anazodo is a William Dawson Scholar and Assistant Professor of Neurology and Neurosurgery at the Montreal Neurological Institute. Dr Anazodo is the Director of the Multimodal Imaging of Neurodegenerative Disease (MiND) Lab at the MNI, where her group develops imaging analysis methods for quantitative neuroimaging. She is the Chair of the Consortium for Advancement of MRI Education and Research in Africa (CAMERA), a global network of imaging experts working to establish sustainable access to MRI in Africa through local capacity building. She is also the Scientific Director of the Medical Artificial intelligence (MAI) Lab at Crestview Radiology Ltd, Lagos, Nigeria, Africa's first machine learning lab dedicated to training, development and implementation of AI applications in medical imaging. Through MiND Lab, MAI Lab, and CAMERA, she is leading several medical imaging and machine learning initiatives across Africa, including the MICCAI Brain Tumor Segmentation (BraTS) Africa Challenge and the Sprint AI Training for African Medical Imaging Knowledge Translation (SPARK) Academy.

  • Dr. Tinashe Mutsvangwa

    Dr. Tinashe Mutsvangwa

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

    Dr. Tinashe Mutsvangwa is a Full Professor and Head of the Department of Data Science at IMT-Atlantique, France, as well as an Adjunct Associate Professor in Biomedical Engineering at the University of Cape Town, South Africa. Specializing in medical imaging and machine learning applications in healthcare, he plays a significant role in exploring healthcare data science within both the African and European contexts. His work focuses on innovating medical imaging techniques and developing machine learning algorithms, particularly addressing the unique challenges faced in African healthcare settings, especially those with limited resources. His expertise in 2D-to-3D image reconstruction and statistical modeling of anatomy from medical images significantly contributes to the development of contextually relevant medical imaging solutions. Committed to fostering interdisciplinary collaboration and mentorship, Dr. Mutsvangwa actively engages in nurturing talent and building capacity in medical imaging and machine learning across continents, aiming to empower healthcare professionals with technology to optimize healthcare delivery and patient outcomes

  • Dr William Ogallo

    Dr William Ogallo

    Google, Nairobi, Kenya

    Dr William Ogallo is a Senior Research Scientist at Google. He focuses on the development and validation of Artificial Intelligence and Machine Learning tools for forecasting food insecurity outcomes and analyzing drivers of food insecurity. Prior to joining Google, William was a senior research scientist and manager at IBM Research Africa in Nairobi. He managed the Discovery Science and Applications team at IBM Research Africa and co-led several projects including the development of a foundation model for small molecule drug design, anomalous pattern detection in tabular healthcare datasets, machine learning for global health, and enabling care continuity via patient-controlled data exchange. William holds a PhD in Biomedical Informatics from Columbia University in the City of New York and a Bachelor of Pharmacy degree from the University of Nairobi.

  • Alessandro Crimi

    Alessandro Crimi

    AGH University of Krakow, Poland

    Dr. Alessandro Crimi is a Professor at the Technical School of Krakow AGH-University of Krakow. He has been teaching and supervising MSc theses at the African Institute of Mathematical Sciences in Ghana and South Africa for 9 years.

    He also conducted several projects in Sub-Saharan Africa related to novel technologies used in rural areas. Dr. Crimi is currently involved in initiatives to promote entrepreneurship with women and individuals with immigrant background in Italy and Poland, as well as technology transfer projects for young scientists to create startup companies.

  • Farouk Dako, MD, MPH

    Farouk Dako, MD, MPH

    Radiology, University of Pennsylvania

    Dr. Dako is a cardiothoracic radiologist and director of global and population health research in Radiology at University of Pennsylvania. He leads multiple community-engaged data-driven research projects in resource-limited settings aimed at making health care delivery and cancer care more equitable. He also leads multiple research projects aimed at improving understanding of cardiovascular disease and cancer manifestation in Africa. Dr Dako is the Nigeria Program Director and Board Member of RAD-AID International, a Radiology NGO. These roles enables his involvement and leadership in capacity-building and education initiatives in Nigeria and other parts of Africa.

Speaker lineup and Schedule to be announced shortly.


  • Consortium for Advancement of MRI Education and Research in Africa (CAMERA)
  • Radiological Soceity of North America (RSNA) R&E Foundation
  • Medical Image Computing and Computer Assisted Intervention Society


Palmeraie Conference Centre, Marrakech, Morocco

Contact us

If you have any questions, please contact info.camera.mri@gmail.com .

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