Foreign-Office |

Summer School on Biomedical Imaging with Deep Learning (BILD)

Sun, 01 Jun 2025 00:00:00 +0000

Medical imaging is central to diagnosis, yet many hospitals in North Africa and the Middle East work with portable devices, limited compute and few specialists. Training the next generation of AI researchers to build methods that work under these conditions — and connecting them across countries — is the goal of the Summer School on Biomedical Imaging with Deep Learning (BILD).

The first edition took place in Tunis, Tunisia, 1–5 September 2025, as part of , the Strategic Arab-German Network for Affordable and Democratized AI in Healthcare. It was organised by the Albarqouni Lab (University Hospital Bonn and University of Bonn) with the University of Tunis El Manar, the Lebanese American University and Duhok Polytechnic University, and funded by the German Academic Exchange Service (DAAD) with funds from the Federal Foreign Office.

Measures

Summer school for about 50 students and graduates from Germany, Lebanon, Iraq and Tunisia, selected through an open call, with around 10 faculty members.
Course modules that combine current AI research with hands-on, resource-aware exercises: deep learning for medical imaging, lightweight neural networks for portable ultrasound, and federated learning on decentralised datasets.
Expert lectures by speakers from academia and industry, including the University of Tunis El Manar, TU Munich, FAU Erlangen-Nürnberg, Imperial College London and NVIDIA.
Hands-on labs and collaborative case studies guided by tutors from Bonn, Tunis and partner institutions.
Co-mentored research projects and exchange sessions in mixed international teams, continuing the collaboration beyond the week in Tunis.

Outcomes

BILD strengthened practical skills in building and deploying AI models for medical imaging, brought together young researchers and faculty from four countries, and grew the SANAD network into a sustained transnational community of AI innovators in healthcare.

Strategic Arab-German Network for Affordable and Democratized AI (SANAD)

Wed, 01 Jan 2025 00:00:00 +0000

In recent years, the transformative potential of Artificial Intelligence (AI) in revolutionizing global healthcare has become evident, offering promising advancements in diagnostic precision, personalized treatment, and healthcare delivery. However, integrating AI into low-resource environments, especially in low- and middle-income countries (LMICs), remains constrained by significant barriers such as limited computational resources, inadequate local data, and financial constraints. The Strategic Arab-German Network for Affordable and Democratized AI in Healthcare (SANAD- سند) seeks to address these challenges by fostering collaboration, empowering local expertise, and advancing Affordable AI and Collaborative Federated Learning solutions tailored to underserved regions. The word “SANAD” means support in Arabic, which is what this project is about.

SANAD aims to bridge the healthcare gap between resource-rich and resource-limited settings, particularly the Global North and Global South disparity. Many existing AI models are developed using centralized datasets from well-funded institutions, which often fail to generalize across diverse populations and contexts. SANAD proposes a strategic, inclusive approach to democratizing access to AI by leveraging interdisciplinary expertise and promoting knowledge-sharing between Arab and German stakeholders

Measures:

, Tunisia
, Germany

Affordable AI and Collaborative Federated Learning for Global Healthcare (EEDA)

Fri, 01 Sep 2023 00:00:00 +0000

Modelling Uncertainty in Deep Learning for Medical Applications

Wed, 01 Jan 2020 00:00:00 +0000

Deep Learning has emerged as a leading technology for accomplishing many challenging tasks showing outstanding performance in a broad range of applications in computer vision and medical applications. Despite its success and merit in recent state-of-the-art methods, DL tools still lack in robustness hindering its adoption in medical applications. Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has been successfully introduced to computer vision for better understanding the underlying deep learning models. In this proposal, we investigate modeling the uncertainty for medical applications given the well-known challenges in medical image analysis, namely severe class-imbalance, few amounts of labeled data, domain shift, and noisy annotations.