African Network for Artificial Intelligence in Biomedical Imaging (AFRICAI)

All researchers and stakeholders from all over the world, including within and outside Africa, are welcomed to join the SIG-AFRICAI. We look forward to collaborating with you to address medical imaging challenges that will enhance access to healthcare across the African continent and beyond.

Mission

The primary mission of the Special Interest Group on the African Network for Artificial Intelligence in Biomedical Imaging (SIG-AFRICAI) is to connect researchers and all stakeholders interested in MICCAI related research in Africa, and to increase research output and activities at MICCAI addressing real-world challenges and applications within Africa. To this end, we will regularly organise discussions, exchanges of ideas and results, webinars and training events, mentorship and other MICCAI related activities for Africa.

Goals

Foster Collaboration among African and International Researchers

Connect African and international researchers, postdocs, students, residents, AI developers, innovators, radiologists, other clinicians, and institutions working in the field of AI for biomedical imaging applications.

Boost African Participation at MICCAI

Increase the number of African researchers participating in MICCAI, by raising awareness and supporting submissions to MICCAI and its satellite events.

Enhance availability of African Medical Image Datasets

Support initiatives such as challenges that will provide access to curated and public real-world medical image datasets from Africa.

Enhance Access to Educational Opportunities

Organize and support tutorials, workshops, and summer schools to encourage increased participation from Africa in MICCAI events.

Board Members

    Karim Lekadir

    University of Barcelona and Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

    ME

    Marawan Elbatel

    The Hong Kong University of Science and Technology, Hong Kong SAR

    Udunna Anazodo

    Montreal Neurological Institute, McGill University

    YM

    Yunusa Muhammed

    Gombe State University, Gombe, Nigeria

    XL

    Xiaomeng Li

    The Hong Kong University of Science and Technology

    KM

    Kaouther Mouheb

    Biomedical Imaging Group Rotterdam

    HZ

    Hasnae Zerouaoui

    College of Computing, Mohammed VI Polytechnic University

    JF

    Jean-Rassaire Fouefack

    MAIA Medical Technologies, France

    AF

    Azade Farshad

    Technical University of Munich

    RC

    Raymond Confidence

    Montreal Neurological Institute, McGill University

    Julia Schnabel

    Helmholtz Munich And Technical University of Munich, Germany

    TM

    Tinashe Mutsvangwa

    IMT Atlantique, Brest, France

    CC

    Celia Cintas

    IBM Research Africa, Nairobi, Kenya

    CK

    Cecilia Kessler

    FM

    Fleur Meijers

    Activities

    MIRASOL Workshop

    A full-day workshop on Medical Image Computing in Resource-Constrained Settings (MIRASOL & KI), bringing together researchers to discuss methods, datasets, and collaborations tailored for low-resource environments.

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    AFRICAI Webinars & Workshops

    Regular sessions to engage the African research community, promote knowledge sharing, and build capacity in AI for biomedical imaging.

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    AFRICAI Journal Club

    Bi-weekly paper presentations with opportunities for networking and recognition (including €150 Best AFRICAI Journal Club Award).

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    Mentorship Awards

    Ongoing support for African students, including mentorship, paper preparation guidance, and funding to attend MICCAI 2026.

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    MICCAI Meets AFRICA Workshop at MICCAI 2024

    A dedicated workshop focused on fostering collaboration between African researchers and the MICCAI community.

    From MICCAI to AFRICAI Session and Networking Event

    A session to discuss African-specific challenges and gather suggestions on bridging MICCAI and AFRICAI initiatives.

    miccai

    The leading international forum for research, education and practice in the field of medical image computing, machine learning in medical imaging, and computer assisted medical interventions and robotics.

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