Albarqouni Lab, located at the Clinic for Diagnostic and Interventional Radiology, University Hospital Bonn and Helmholtz AI, Helmholtz Munich, aims to develop the next generation of AI in Medicine, specifically computational imaging algorithms with a particular focus on artificial intelligence (AI) and machine learning (ML) models to improve the clinical workflow for patients, radiologists, and healthcare professionals. The lab is affiliated with the Munich School for Data Science (MUDS), the Medical Imaging Center Bonn (MIB), and the European Laboratory for Learning and Intelligent Systems (ELLIS).

The research at the Albarqouni Lab focuses on three main areas: computational medical imaging, federated learning in healthcare, and affordable AI and healthcare.

  • In the area of computational medical imaging, the goal is to develop automated solutions that improve efficiency and accuracy in medical imaging, taking into account challenges such as limited annotated data, low inter- and intra-observer agreement, imbalanced class distribution, variability between scanners, and domain shift.
  • In the area of federated learning in healthcare, the focus is on developing deep learning algorithms that can share knowledge among AI agents in a robust and privacy-preserving manner, while also addressing issues such as data heterogeneity, explainability, quality control, and robustness to poisoning attacks.
  • Finally, the lab is also interested in developing affordable AI solutions suitable for use with low-quality data from low-infrastructure and point-of-care settings.

Meet the lab members

Computational Imaging Reserach (Albarqouni Lab)

Principal Investigator


Shadi Albarqouni

Professor of Computational Medical Imaging Research at University of Bonn | AI Young Investigator Group Leader at Helmholtz AI

AI in Medicine, Federated Learning, Affordable AI and Healthcare



Anna Lisitsyna

Research Scientist

Machine Learning for healthcare, Computer Vision, Federated Learning


David D. Gaviria

PhD Candidate

Machine Learning for Medical Imaging, Computer Vision


Manuela Bergau

PhD Candidate

Machine Learning and Data Science for Medical Imaging, Natural Language Processing


Razieh Rezaei


Machine Learning for Medical Imaging, Vision-language Models, Explainable AI


Sarah Schaab

Master Student at LMU Munich, Munich, Germany

Machine Learning for Medical Imaging, Computer Vision, Natural Language Processing



Carlos Fernández Del Cerro

Visiting PhD Student

Deep Learning in Medical Imaging, Image Reconstruction, Federated Learning, Weakly Supervised Learning


Hyun Ko

Visiting Radiology Clinician Reseacher

Machine and Deep Learning for Medical Imaging, Radiomics


Mazen Soufi

Visiting Scientist

Data science and Deep learning for medical image analysis, Disease modeling, Musculoskeletal system analysis


Nada El Darra

Visiting Scientist

Food Safety, Food Technology, Artifical Intelligence


Qiongkai Xu

Research Fellow of Security in NLP

Natural Language Processing, Machine and Deep Learning, Security and Privacy, Evaluation Theorem

Latest News

Research Projects

Funded and Active Projects. Thanks to our great collaborators!

Affordable AI and Healthcare

We are also interested in developing affordable AI solutions suitable for poor-quality data generated by low infrastructure and point-of-care diagnosis.

BigPicture Project

The 6-year, €70 million project called BIGPICTURE will herald a new era in pathology

Deep Federated Learning in Healthcare

One of our recent and promising projects.

Learn from Crowds

Crowdsourcing, Gamification

Learn from Prior Knowledge

Manifold Learning, Graph Convolutional Networks

Learn to Adapt

Domain Adaptation, Style Transfer

Learn to Learn

Meta-Learning, Few-Shot Learning

Learn to Reason and Explain

Interpretable ML, Disentangled Representation, Fairness

Learn to Recognize

Detection, Classification, Segmentation, Anomaly Detection, Semi-/Weakly-Supervised Learning

Modelling Uncertainty in Deep Learning for Medical Applications

DAAD PRIME Fellowship at ETH Zürich and Imperial College London

Telemedicine in Palestine

Telemedicine in Palestine from Shadi Nabil Albarqouni Collaboration: Funding:


Teaching courses, Theses, and Internships

BA/MA Thesis: Deep Learning for Inherited Retinal Diseases Detection

Abstract. Our project aims to improve the diagnosis of Inherited Retinal Dystrophies (IRDs), a group of rare retinal diseases impacting over 2 million people globally [1]. IRDs can lead to vision problems like night blindness, color blindness, tunnel vision, and eventual blindness, greatly affecting patients’ and their families’ quality of life [4].

MA Thesis: Deep Learning based detection model of the temporal and axillary artery in suspected giant cell arteritis in ultrasound images

Abstract. Giant cell arteritis (GCA) is a systemic autoimmune disease marked by inflammation of blood vessels (“vasculitis”) that can cause impairment and damage to organs [1]. GCA typically affects large and medium size arteries, such as the aorta and the temporal and axillary arteries [2–4].

MA Thesis: Deep Learning based model for detection and grading of prostate cancer using mpMRI and MR-Fingerprinting

Abstract. Prostate cancer (PCa) is the most common cancer in men and the second leading cause of cancer death in Germany [4,14]. Both digital rectal examination (DRE) along with the prostate-specific antigen (PSA) level in blood samples are typically used in PCa screening.

MA Thesis: Deep Learning-based method for virtual ECV in cardiac magnetic resonance imaging

Abstract. Diseases of the cardiovascular system are among the most common diseases worldwide and are the leading cause of death. The World Health Organization (WHO) estimates that about 17.9 million people die of cardiovascular diseases each year worldwide.

Course: Introduction to Machine Learning

Machine Learning has gained a lot of momentum within development organizations that are actively looking for innovative solutions to leverage their data to identify new levels of understanding their operations and processes. Machine learning is a subfield of Artificial Intelligence where the machine learns from data rather than from explicit programming.

Community Engagement

Professional Services and Invited Talks

Invited Talk at the Workshop on Collaborative Learning: From Theory to Practice

I had the pleasure to give an invited talk at the #Collaborative Learning workshop at MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)! It was a wonderful weekend full of amazing talks and fruitful discussions! I had the pleasure to meet a few familiar faces in our community along with other great speakers from UC Berkeley, Harvard, MIT, KAUST, ETH Zurich, Nvidia, and EPFL, among others. I would like to thank Michael I. Jordan and the organizing team behind the workshop for the invitation and the excellent hospitality! For those who are interested in the talks, they will be made publicly available soon!

Invited Talk at AI 4 Imaging

I will deliver a talk in Federated Deep Learning in Healthcare

Attending the general assembly of the Arab German Young Academy

I had the pleasure to meet such brilliant scientists from all over the Arab world and Germany in the general assembly of the Arab-German Young Academy of Sciences and Humanities in Berlin!

Co-Organizing the 10. DFG-#Nachwuchsakademie

We had a wonderful week (10. DFG-#Nachwuchsakademie) with many insightful talks and fruitful discussions at the Schloss Birlinghoven! 20 participants with medical and technical backgrounds, from all over Germany, came together to learn about #AI in #Medicine!

Open Positions

Admin., Research Assistants, PhDs, Postdocs, and Medical Research Assistants. We do accept applications on an on-going basis, so feel free to send your motivation letter, resume, and your transcript via email.

CIR/06/2023: Ph.D. position in AI in Medicine - Robustness and Explainability (m/f/d)

starting July 2023 or as agreed upon. The position is initially limited to three years, with the possibility of extension.


  • Venusberg-Campus 1, Bonn, DE- 53127
  • Building 07, UniKlinikum Bonn, University of Bonn