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.

Summer School on Affordable AI (SAAI)

Wed, 01 May 2024 00:00:00 +0000

Artificial intelligence could ease the shortage of specialists and diagnostic capacity in many health systems, yet most AI courses assume powerful hardware, large datasets and stable infrastructure that are scarce in low- and middle-income countries of the Middle East and North Africa (MENA). The Summer School on Affordable AI (SAAI, سعي — “striving”) was designed to close this gap by teaching AI that works under real-world resource constraints.

SAAI took place in Bonn, 22–26 July 2024, and was organised by the AGYA working group Innovation, in close collaboration with the working group Health & Society, of the Arab-German Young Academy of Sciences and Humanities (AGYA), with the University of Bonn, the University Hospital Bonn and Helmholtz AI. It was hosted by the Albarqouni Lab and modelled on our earlier .

Programme

Participants: about 30 students and graduates and 12 faculty members from Germany and Arab countries, selected through an open call.
Foundations: Python programming, mathematics and statistics for machine learning, and analytical thinking.
Affordable AI: teaching modules tailored to the needs and resource limitations of low-income MENA countries, addressing shared health challenges in Germany and the Arab world.
Hands-on learning: lectures, hands-on labs and real-world challenges, worked on in mixed teams with faculty and experienced tutors.

Outcomes

Participants built practical skills in developing AI models for health under limited compute and data, and the school fostered knowledge transfer and collaboration between young researchers from Germany and the Arab world. SAAI laid the groundwork for the DAAD-funded and the in Tunis in 2025.

Summer School |

Summer School on Biomedical Imaging with Deep Learning (BILD)

Measures

Outcomes

Summer School on Affordable AI (SAAI)

Programme

Outcomes