AGYA Member

Shadi Albarqouni

Technical Sciences

Areas of Expertise:
Medical Imaging with Deep Learning, Computer Vision, Uncertainty Quantification, Low-Data Regime, Active Learning, Federated Learning

About me

I am a computer scientist based at the Helmholtz Center Munich and the Technical University of Munich (TUM). I am the leader of an Artificial Intelligence Young Investigator Group at Helmholtz AI and TUM Junior Fellow at the Faculty of Informatics and TUM School of Medicine. In the last few years, I have been leading a team of PhD students working on developing Machine Learning algorithms for clinical applications to mitigate the challenges in Medical Imaging. In the future, I will be working on developing innovative deep Federated Learning algorithms. My goal is to develop algorithms that are capable of distilling and sharing knowledge among AI agents in a robust and privacy-preserving way.

AGYA offers me incredible opportunities for interdisciplinary research collaboration. These collaborations can help to improve the application of AI in healthcare. The bilateral exchange, joint workshops and capacity building seminars help me to transfer my knowledge and develop affordable AI solutions for healthcare in the Arab countries and Germany.

Current projects

  • Member of the AGYA Working Group Innovation
  • Member of the AGYA Working Group Health and Society

Academic Career

2020 -AI Young Investigator Group Leader, Helmholtz AI, Helmholtz Center Munich,
TUM Junior Fellow at the Faculty of Informatics, and TUM School of Medicine
at Technical University of Munich, Germany
2020Helmholtz AI Young Investigator Group, Helmholtz Association
2020Visiting Scientist, Department of Computing, Imperial College London, UK
2019 - 2020Visiting Scientist, Department of Information Technology and
Electrical Engineering, ETH Zurich, Switzerland
2019Postdoctoral Researchers International Mobility Experience (PRIME)
Fellowship, German Academic Exchange Services (DAAD)
2017 - 2020Senior Research Scientist and Team Lead,
Chair for Computer Aided Medical Procedures (CAMP),
Technical University of Munich, Germany
2017Ph.D. in Computer Science, Technical University of Munich, Germany
2013 - 2017 Visiting Researcher, German Center for Neurodegenerative Diseases (DZNE), Germany
2013 - 2017Research Scientist and Team Lead,
Chair for Computer Aided Medical Procedures (CAMP),
Technical University of Munich, Germany
2011 - 2012Part-time Lecturer, Electrical and Computer Engineering Department,
Islamic University of Gaza, Palestine
2010M.Sc. in Electrical Engineering, Islamic University of Gaza, Palestine


Selected Publications

  • Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S. and Navab, N., 2016. Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE transactions on medical imaging35(5), pp.1313-1321. (PDF)
  • Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M.N., Landman, B., Maier-Hein, K., Ourselin, S., et al.  2020. The future of digital health with federated learning. npj Digital Medicine, 3 (119), Nature (PDF)
  • Sarhan, M.H., Navab, N., Eslami, A. and Albarqouni, S., 2020. Fairness by Learning Orthogonal Disentangled Representations. In European Conference in Computer Vision (ECCV 2020). IEEE. (PDF)
  • Shaban, M.T., Baur, C., Navab, N. and Albarqouni, S., 2019, April. Staingan: Stain style transfer for digital histological images. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 953-956). IEEE. (PDF)
  • Degel, M.A., Navab, N. and Albarqouni, S., 2018, September. Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018) (pp. 630-637). Springer, Cham. (PDF)


See a full list of publications


Technical University Munich & ETH Zurich

Helmholtz AI Helmholtz Center Munich Ingolstädter Landstraße 1 D-85764, Neuherberg

+49 (0) 89 3187 43862

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