Prof. Dr. Daniel Rückert

Professorship

Artificial Intelligence in Healthcare and Medicine

Academic Career and Research Areas

Professor Rückert’s (*1969) field of research is the area of Artificial Intelligence (AI) and Machine Learning and their application to medicine and healthcare. His research focuses on (1) the development of innovative algorithms for biomedical image acquisition, image analysis and image interpretation – especially in the areas of image reconstruction, registration, segmentation, traching and modelling; (2) AI for extracting clinically useful information from biomedical images – especially for computer-assisted diagnosis and prognosis.

Since 2020, Daniel Rückert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich. He is also a Professor at Imperial College London. He gained a MSc from Technical University Berlin in 1993, a PhD from Imperial College in 1997, followed by a post-doc at King’s College London. In 1999 he joined Imperial College as a Lecturer, becoming Senior Lecturer in 2003 and full Professor in 2005. From 2016 to 2020 he served as Head of the Department of Computing at Imperial College.

    Awards

    • Alexander von Humboldt Professor (2020)
    • Fellow, Academy of Medical Sciences (2019)
    • Fellow, Royal Academy of Engineering (2015)
    • Fellow, IEEE (2015)
    • Fellow, MICCAI (2014)

    Key Publications (alle Publikationen)

    Rueckert, D. and Schnabel, J. A.: “Model-Based and Data-Driven Strategies in Medical Image Computing”. Proceedings of the IEEE. 2020; 108(1): 110-124.

    Abstract

    Kaissis, G. A., Makowski, M. R., Rueckert, D. and Braren, R. F.: “Secure, privacy-preserving and federated machine learning in medical imaging”. Nature Machine Intelligence 2. 2020; 305–311.

    Abstract

    Bello, G. A., Dawes, T. J. W., Duan, J., Biffi, C., de Marvao, A., Howard, L. S. G. E., Gibbs, J. S. R., Wilkins, M. R., Cook, S. A., Rueckert, D. and O'Regan, D. P.: “Deep learning cardiac motion analysis for
    human survival prediction”. Nature Machine Intelligence. 2019; 1:95-104.

    Abstract

    Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B. and Rueckert, D.: “Attention gated networks: Learning to leverage salient regions in medical images”. Medical Image Analysis. 2019; 53:197-207.

    Abstract

    Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N. and Rueckert, D.: “A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction”. IEEE Transactions on Medical Imaging. 2018; 37(2): 491-503.

    Abstract