activeFunded · 2026 · MISTAI
MISTAI: Medical Intelligence & Skills Training in AI
Bilateral UKB–Dnipro State Medical University project, funded by the NRW Ministry of Culture and Science, building an AI pipeline and training programme in …
Albarqouni Lab — Computational Medical Imaging Research (CoMI), University of Bonn
- 172
- publications
- 20,826
- citations · h-index 45
- 41
- projects · 11 active
- 58
- partners · 29 countries
38 team members & collaborators today>30 researchers trained over 5 years6 PIs Computational AG at BonnJour
Explore team →12 curated clinical datasets & benchmarks13 institutions · 6 clinics4K patients
Explore datasets →8× A100 GPUs, 64 GB, at Bonn12 AI services in the CoMI marketplace (internal)16 OMI partners across Germany, 4 MII consortia
Explore infrastructure →>50 students over 5 years0 block courses and schools: EEDA, SAAI, BiLD, AiM50 course offerings · joint teaching with GUC and LAU
Explore teaching →41 research collaborations & funded projects · 11 active1 agenda imaging, responsible and federated AIDFG · BMFTR · MKW NRW · AGYA funders of our ongoing projects
Explore funding →172 publications · 17 in Q1 journals60 journal articles66 conference papers (MICCAI, NeurIPS, AAAI, ISBI…)
Explore venues →50 invited talks, incl. 12 keynotes24 schools, workshops & committees organised29 countries, 5 continents
Explore community →58 partner institutions29 countries41 joint projects
Explore partners →Research themes
All themes →
Computational Medical Imaging
Medical foundation models, multimodal imaging and uncertainty — methods that turn images into reliable clinical evidence.
161 papers
34 projects
55 courses
26 resources
Affordable & Responsible AI
Fairness and robustness across populations and resource settings — AI that is affordable, trustworthy and works for everyone.
60 papers
24 projects
6 courses
10 resources
Federated Learning
Multi-centre evidence without moving clinical data — collaborative, privacy-preserving learning across hospitals.
24 papers
11 projects
8 courses
8 resourcesLatest news & media
All media coverage →
Photo: Al-Quds
Nov 2026Al-Quds newspaper on the Düsseldorf conference of the Friends of Al-Quds University in Germany (02.11.2026)
Photo: Al-Quds
Oct 2026 · Düsseldorf, GermanyKeynote and Panel Discussion at the Annual Conference of the Friends of Al-Quds University in Germany
Oct 2026 · Cairo, EgyptKeynote on Resilience and Higher Education in Gaza at the DAAD Regional Office Cairo
Oct 2026 · Bonn, GermanyMedical Pitch Contest Bonn
Oct 2026 · Cologne, GermanyInvited Lecture and Panel at the 3rd Arab Science Symposium in Germany (ASSG 2026) in Cologne
Oct 2026 · Bonn, GermanyPanel and Workshop on the Academic Reconstruction of Gaza with DAAD and KAAD Alumni
Photo: Albarqouni Lab
Sep 2026 · Strasbourg, FranceOur Lab at MICCAI 2026 in Strasbourg
Sep 2026 · Strasbourg, FranceTwo Awards to Attend MICCAI 2026 in StrasbourgActive projects
All projects →
active
activeFunded · 2025 – ongoing · Eyelid Tumour AI
KI-gestützte Diagnostik von Tumoren am Auge: Dignitätseinordnung von Lidtumoren mittels KI zur optimierten Patienteneinbestellung
Prototyping grant of the University of Bonn (Transfer Center enaCom) to develop an app that supports the assessment and classification of eyelid tumours — …
activeFunded · 2025 – 2026
Application of AI and Advanced MRI for a Better Prediction of Disease Progression in Neurological Disorders
Bonn International Fellowships (2024 - 2025)
activeResearch collaboration · 2024 – ongoing
MRIQT: Image-quality transfer from ultra-low-field to high-field neonatal MRI
This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio …
activeResearch collaboration · 2024 – ongoing
INTERACT-CMIL: Interpretable multi-task grading of conjunctival melanocytic lesions
This project addresses the grading of conjunctival melanocytic intraepithelial lesions (CMIL), rare precursors of conjunctival melanoma characterized by …
activeResearch collaboration · 2024 – ongoing
Bias-aware foundation models for equitable breast cancer screening
This project addresses fairness and generalization challenges of foundation models for breast mammography, where AI systems trained predominantly on data from …
Featured publications
JACR · 2026
Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper
Q1IF 5.3❝
IEEE TMI · 2023
Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models
Q1IF 12.4❝
Nature Mach. Intell. · 2022
Federated disentangled representation learning for unsupervised brain anomaly detection
Q1IF 23.9❝
Medical Image Analysis · 2026
When multi-institutional dataset aggregation masks shortcut-like behavior in Foundation Models for medical imaging
Q1IF 11.8❝
Scientific Reports · 2026
Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer
Q1IF 4.9❝
The BMJ · 2025
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Q1IF 42.7Recent publications
All publications →U-Net-Based Prediction of Concealed Volume Loss in AZ31 From Optical Corrosion Imprints and As-Exposed Surface Topography
Advanced Intelligent Discovery, 2026
Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper
Journal of the American College of Radiology, 2026
Improving diagnosis and interpretability in chest X-rays through federated learning and adversarial training
BMC Medical Informatics and Decision Making, 2026
PromptGate: Client-Adaptive Vision–Language Gating for Open-Set Federated Active Learning
Medical Image Computing and Computer Assisted Intervention – MICCAI 2026, Lecture Notes in Computer Science, 2026
K-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging
Medical Image Computing and Computer Assisted Intervention – MICCAI 2026, Lecture Notes in Computer Science, 2026
Histopathologische Klassifikation konjunktivaler melanozytärer intraepithelialer Läsionen (CMIL) mit einem Deep-Learning-Algorithmus
124th DOG Congress (Deutsche Ophthalmologische Gesellschaft), Berlin, 24–27 September 2026, 2026
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