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

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Shadi Albarqouni

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

AI in Medicine, Federated Learning, Affordable AI and Healthcare

Researchers

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David D. Gaviria

PhD Candidate

Machine Learning for Medical Imaging, Computer Vision

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Elodie Germani

Postdoctoral Researcher

Medical imaging, Representation learning, Data harmonization, Robustness of AI methods

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Yiheng Xiong

PhD Candidate

Medical Imaging Analysis, Computer Vision, Machine Learning

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Mert Ikinci

Master Thesis Student

Machine Learning for Medical Imaging, Deep Learning, Computer Vision

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Malek Al Abed

Master Thesis Student and Research Assistant (HiWi)

Artificial Intelligence for Healthcare, Deep Learning, Computer Vision

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Anna Lisitsyna

Research Scientist

Machine Learning for healthcare, Computer Vision, Federated Learning

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Elyes Farjallah

Research Assistant (HiWi)

Machine Learning for Medical Imaging, Computer Vision

Clinical Collaborators

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Valentin Schäfer

Professor and Head of the Department of Rheumatology and Clinical Immunology at the Clinic of Internal Medicine III at Universitätsklinikum Bonn

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Hyun Ko

Radiology Clinician Reseacher at Peter MacCallum Cancer Centre, Australia

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Mertina Herwig-Carl

Professor, Oberärztin der Augenklinik am Universitätsklinikum Bonn

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Barbara Wichtmann

Fachärztin für Radiologie at Universitätsklinikum Bonn

Alumni

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Diletta Chiaro

Visiting PhD Student

Federated Learning (theory and applications), Machine Learning for Industry and Education 4.0

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Sarah Schaab

Master Student and Research Assistant (HiWi)

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

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Carlos Fernández Del Cerro

Visiting PhD Student

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

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Razieh Rezaei

Researcher

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

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Nada El Darra

Visiting Scientist

Food Safety, Food Technology, Artifical Intelligence

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Manuela Bergau

Researcher

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

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Cosmin Bercea

Researcher

Deep Learning for Medical Image Analysis, Anomaly Detection, Federated Learning, Image Understanding

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Tariq Bdair

Researcher

Machine Learning in Medical Imaging, Semi-Supervised Learning, Federated Learning

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Tobias Bernecker

Master Student

Deep Learning for Medical Image Analysis, Federated Learning, Domain Adaptation

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Anna Bodonhelyi

Research Assistant (HiWi)

Federated Learning, Machine Learning for healthcare, Computer Vision

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Mazen Soufi

Visiting Scientist

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

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Qiongkai Xu

Visiting PhD Student and Research Fellow of Security in NLP

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

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Simone Löhlein

Master Student

Neuroscience, Machine Learning for healthcare, Deep Learning

Latest News

Research Themes

Active Research Themes. 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.

Deep Federated Learning in Healthcare

This 5 years Helmholtz funded project to advance the field with Federated Learning algorithms in Medicine (2020-2025)

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

Funded Research Projects

Thanks to the 3rd party funding from EU, DFG, DAAD, BMBF, AGYA, GIZ and BaCaTec

Summer School on Affordable AI (SAAI)

BMBF Funded Project with the Arab-German Young Academy for Science and Humanities (2024-2024)

Open Medical Inference (OMI)

BMBF Funded Project through Medical Informatics Initaitive (MII) consortia (2024-2028)

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

DAAD Funded Project with Beirut Arab University, Lebanon (2023-2023)

Deep learning to estimate aging from chest imaging

The 3-year DFG Funded project with UniKlinikum Freiburg (2023-2026)

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

The 3-year DFG Funded project with LMU and TU Munich (2022-2025)

BigPicture Project

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

Deep Federated Learning in Healthcare

This 5 years Helmholtz funded project to advance the field with Federated Learning algorithms in Medicine (2020-2025)

Modelling Uncertainty in Deep Learning for Medical Applications

DAAD Funded Project with ETH Zürich and Imperial College London (2020-2022)

Telemedicine in Palestine

Telemedicine in Palestine from Shadi Nabil Albarqouni Collaboration: Funding:

Uncertainty Aware Methods for Camera Pose Estimation and Relocalization

BaCaTeC Funded Project with Stanford University and Siemens AG (2020-2021)

Students

Teaching courses, Theses, and Internships

MA Thesis: Deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with lymph node metastasis (LNM) being a critical determinant in patient prognosis and therapeutic planning [1-2]. Conventional methods for detecting LNM in PDAC primarily rely on contrast-enhanced CT scans, but these often fall short in sensitivity, especially in early-stage disease.

Seminar: Artificial Intelligence in Radiology: Applications and Research

The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on Artificial Intelligence in Radiology: Applications and Research. This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.

MA Thesis: Development of a Machine Learning Algorithm for Histopathological Classification of Conjunctival Melanocytic Intraepithelial Lesions -- Not available

Abstract. Conjunctival Melanocytic Intraepithelial Lesions (CMIL) are a significant precursor to conjunctival melanoma, a rare but potentially fatal ocular cancer. The histopathological classification of CMIL is crucial for early diagnosis and treatment planning.

Call for Application for DAAD AInet Fellowship

We are excited to announce that Albarqouni Lab is proud to be one of the host institutions for the prestigious DAAD AInet Fellowship, which is awarded twice a year to outstanding international early-career researchers in artificial intelligence. This fellowship offers a unique opportunity to join the Postdoctoral Networking Tour in Artificial Intelligence (Postdoc-NeT-AI), where awardees can engage with leading researchers in Germany, fostering collaborations and creating new research and career opportunities. At Albarqouni Lab, we are at the forefront of AI research in medical imaging, machine learning, and large language models, and have successfully hosted several AInet Fellows in the past. We invite interested applicants to apply and consider joining us to advance cutting-edge AI research in healthcare. More details can be found here.

MA Thesis: Investigating Bias in AI Algorithms for Breast Cancer Detection from Mammography Imaging: A Focus on Generalization to Unseen Populations

Abstract. Breast density is a critical factor in breast cancer risk and detection, influencing the effectiveness of mammography. Higher breast density, characterized by a greater proportion of fibroglandular tissue relative to fatty tissue, is associated with a four- to sixfold increase in breast cancer risk.

Community Engagement

Professional Services and Invited Talks

Moderator at the MIB Future Panel 2024

It was a pleasure moderating the Symposium on AI in Medicine and sharing our recent works, which have recently been accepted at hashtag#MICAD and hashtag#MICCAI Workshops! Thanks to the team members Sarah Schaab, David D. Gaviria, and Elyes Farjallah for their great efforts! Thanks to the co-moderator Prof. Valentin S. Schäfer, and the organizing team behind the MIB Future Panel; Lisa Mona Marie Senner, Prof. Frank G. Holz, and the dean, Prof. Bernd Weber, among others! Congrats to the winners!

David participated at the MIB Future Panel in Bonn

It was an exciting to present our research on Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma at the MIB Future Panel 2024. Hosted by the Medical Imaging Center Bonn in partnership with Universitätsklinikum Bonn and the Medical Faculty of Rheinische Friedrich-Wilhelms-Universität Bonn, this symposium united thought leaders from both academia and industry.

David participated with a poster presentation at the Student Retreat from BIGS Clinical and Population Science PhD program

David participated in the Student Retreat 2024 organized by the BIGS Clinical and Population Science PhD program in Bonn, Germany. This event was an invaluable opportunity to present my research in a poster format, engage with fellow scholars, and exchange scientific knowledge.

Elodie attended and presented at the 2024 International Conference on Image Processing (ICIP) in Abu Dhabi, UAE

Elodie had the chance to attend the 2024 IEEE International Conference on Image Processing in Abu Dhabi, Emirates. She presented her work during an oral session about Medical Image Analysis. This work was part of her Ph.D. projects and was entitled Uncovering communities of pipelines in the task fMRI analytical space.

Invited Keynote at the 1st International NEU DESAM Biotechnology Congress in Nicosia

I had the pleasure to give a talk at the DESAM Congress at the Near East University in Nicosia. I had the chance to meet and discuss further collaboration with rectorate reprsented by the rector, Prof. Dr. Tamer ŞANLIDAĞ, vice rector; Prof. Dr. Umut AKSOY, and his advisors; Prof. Dr. Murat SAYAN and Assoc. Prof. Dr. Dilber UZUN ÖZŞAHİN at the Near East University.

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 or here.

CIR/01/2024: Ph.D. position in AI in Medicine with Large Language Models (m/f/d)

starting Dec 2024 or as agreed upon. The position is initially limited to three years, with the possibility of extension.

CIR/02/2024: Research Manager (m/f/d)

starting Nov 2024 or as agreed upon. The position is initially limited to two years, with the possibility of extension.

AGYA/03/2024: Multiple Research Assistant Openings in Machine/Deep Learning for Interdisciplinary AI Research (m/f/d)

starting mid. Oct 2024 or as agreed upon. Service Contracts are limited to 3 months, with a possiblilty of an extension. The applicant must be based either in Arab nation or Europe.

CIR/03/2024: Postdoc/Research Staff position in Computational Medical Imaging (m/f/d)

starting Nov. 2024 or as agreed upon. The position is initially limited to two years.

Contact

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