Shadi Albarqouni
Prof. Dr. rer. nat. Shadi Albarqouni
Zugehörigkeiten
  • Klinik für Diagnostische und Interventionelle Radiologie
Forschungsschwerpunkte
  • computer vision
  • deep learning
  • machine learning
  • medical imaging
Shadi Albarqouni is a Professor of Computational Medical Imaging Research at the University of Bonn and an AI Young Investigator Group Leader at Helmholtz AI. Previously, he worked as a Visiting Scientist at Imperial College London and ETH Zurich, and as a Senior Research Scientist & Team Lead at the Technical University of Munich (TUM). Shadi has more than 100 publications (Citations > 9000, h-index: 33) in Medical Imaging Computing, Computer Vision, and Machine Learning published in MedIA, IEEE TMI, IEEE JBHI, IPMI, MICCAI, MIDL, ISBI, ICCV, ECCV, BMVC, NeurIPS, AAAI, and ICML. Shadi has been actively serving the community with different roles as a reviewer, session chair, area chair, program committee member, organizing committee member, and Program co-chair at MICCAI, MIDL, and ISBI, among other conferences. He has served as an Associate Editor for Special Issues at IEEE Transactions on Medical Imaging and Medical Image Analysis.
Ausgewählte Publikationen

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 imaging, 35(5), pp.1313-1321.

Baur, C., Denner, S., Wiestler, B., Navab, N. and Albarqouni, S., 2021. Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Medical Image Analysis, 69, p.101952.

Bercea, C.I., Wiestler, B., Rueckert, D. and Albarqouni, S., 2022. Federated disentangled representation learning for unsupervised brain anomaly detection. Nature Machine Intelligence, 4(8), pp.685-695.

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H.R., Albarqouni, S., Bakas, S., Galtier, M.N., Landman, B.A., Maier-Hein, K. and Ourselin, S., 2020. The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.

Shadi Albarqouni
Prof. Dr. rer. nat. Shadi Albarqouni

C74

Venusberg-Campus 1

53127 Bonn

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