Prof. Shadi Albarqouni
Professor for Computational Medical Imaging Research at the University of Bonn.
Director of Albarqouni Lab.
MICCAI-RISE Network Co-founder
MICCAI 2024-2026 Organizing Committee Member
Elected Member at GYA, AGYA, ELLIS and GLOHRA
Department of Diagnostic and Interventional Radiology, University Hospital Bonn.
Albarqouni worked as a Senior Research Scientist & Team Lead at CAMP leading the Medical Image Analysis (MedIA). Together with his team addressed the common challenges concern the nature of medical data, namely heterogeneity, severe class-imbalance, few amounts of annotated data, inter-/intra-scanners variability (domain shift), inter-/intra-observer disagreement (noisy annotations). In 2019, Albarqouni received the prestigious P.R.I.M.E. fellowship for a one-year international mobility, where he worked as a Visiting Scientist at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zürich, Switzerland. He worked with Prof. Ender Konukoglu on Modeling Uncertainty in Medical Imaging, in particular, the one associated with inter-/intra-raters variability. Afterwards, Albarqouni worked as a Visting Scientist with Prof. Daniel Rueckert at the Department of Computing at Imperial College London, United Kingdom.
Since Nov. 2020, Albarqouni has been appointed as an AI Young Investigator Group Leader at Helmholtz AI. The aim of Albarqouni’s Lab. is to develop innovative deep Federated Learning algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved fashion. Since Jan. 2022, Albarqouni has been appointed as a W2 Professor of Computational Medical Imaging Research at the Faculty of Medicine, University of Bonn.
Albarqouni has more than 120 peer-reviewed publications in both Medical Imaging Computing and Computer Vision published in high impacted journals and top-tier conferences (h-index: 41, citations: >14000). He is an active member of MICCAI, BMVA, IEEE EMBS, IEEE CS, and ESR society. Recently, Albarqouni has been elected as a member for the European Lab for Learning and Intelligent Systems (ELLIS), the Global Young Academy (GYA), the Arab German Young Academy (AGYA), and the Higher Council for Innovation and Excellence in Diaspora (HCIE). Since 2024, he has been elected as a Steering Committee Member at the German Alliance for Global Health Research (GLOHRA). Since 2015, he has been serving as a PC member for a couple of MICCAI workshops, e.g., COMPAY, DART, DCL, FAIR among others. Since 2019, Albarqouni has been serving as an Area Chair in Advance Machine Learning Theory at MICCAI. Albarqouni has been serving as a Program Co-Chair at MIDL’22 in Swizterland, an Organizing Committee Member at ISBI’22 in India, and has been on board the organizing committee of MICCAI since 2024. His current research interests include Interpretable ML, Robustness, Uncertainty, and Federated Learning. He is also interested in Entrepreneurship and Startups for Innovative Medical Solutions with limited resources and leading SANAD initiative to promote AI democratization and knowledge transfer between German and Arab Scholar.
Leveraging Collective Intelligence for Inclusive Global Healthcare
Deep Learning (DL) stands at the forefront of artificial intelligence, revolutionizing computer science with its prowess in various tasks, especially in computer vision and medical applications. Yet, its success hinges on vast data resources, a challenge exacerbated in healthcare by privacy concerns. Federated Learning, a groundbreaking technology, transforms how DL models are trained without compromising data security. By allowing local hospitals to share only trained parameters with a centralized DL model, Federated Learning fosters collaboration while preserving privacy. However, hurdles persist, including heterogeneity, domain shift, data scarcity, and multi-modal complexities inherent in medical imaging. In this talk, we delve into the clinical workflow and confront the common challenges facing AI in Medicine. Our focus then shifts to Federated Learning, exploring its promise, pitfalls, and potential solutions. Drawing from recent breakthroughs, including a compelling MR Brain imaging case study published in Nature Machine Intelligence, we navigate the landscape of secure and efficient AI adoption in healthcare.
