MICCAI Meets Africa Workshop

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 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.

Aim:

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:

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 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: TBA 2024, 11:59 PM PST

Workshop Date: October 6th 2024

Registration:

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.

Organizers

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

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

University of Cape Town, South Africa

Dr. Tinashe Mutsvangwa is an Associate Professor in Biomedical Engineering at the University of Cape Town, South Africa. With a specialisation in medical imaging and machine learning applications in healthcare, he plays a significant role in exploring healthcare data science within the African context. His work centres on innovating medical imaging techniques and machine learning algorithms, focusing particularly on addressing the unique challenges faced in African healthcare settings, especially those with limited resources. His expertise in 2D-to-3D image reconstruction and statistical modelling of anatomy from medical images contributes significantly to the development of contextually relevant medical imaging solutions for Africa. Committed to fostering interdisciplinary collaboration and mentorship, Dr. Mutsvangwa is actively engaged in nurturing talent and building capacity in medical imaging and machine learning throughout the continent, aiming to empower African healthcare professionals with technology to optimise healthcare delivery and patient outcomes.
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

AGH University of Krakow, Sano center for computational medicine

Dr. Alessandro Crimi leads the neuroimaging lab at Sano the Centre for Computational Medicine in Poland. He 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.