37 - Beyond the Patterns - Daniel Rückert - AI and the future of Radiology/ClipID:35899 previous clip next clip

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Recording date 2021-07-22

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Friedrich-Alexander-Universität Erlangen-Nürnberg

Format

lecture

It’s a great pleasure to have Daniel Rückert as a guest speaker in our seminar series!

Abstract: Artificial Intelligence (AI) is changing many fields across science and across our society. In this talk, we will discuss how AI is and will change medicine and healthcare, in particular in the field of radiology. In particular, I will focus on how AI can support the early detection of diseases in medical imaging as well as help with improved diagnosis and personalised therapies. I will a.so describe how deep learning can be used for the reconstruction of medical images from undersampled data, image also super-resolution, image segmentation and image classification in the context of cardiac, fetal and neuroimaging. Furthermore, we will discuss how AI solutions can be privacy-preserving while also providing trustworthy and explainable solutions for clinicians. Finally, I will discuss future developments and challenges for AI in radiology and medicine more generally.

Short Bio: Professor Rückert’s 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.

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

C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging, 38(1):280-290, 2019.

C. F. Baumgartner, K. Kamnitsas, J. Matthew, T. P. Fletcher, S. Smith, L. M. Koch, B. Kainz and D. Rueckert. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound. IEEE Transactions on Medical Imaging, 36(11): 2204 – 2215, 2017.

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

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

W. Bai, H. Suzuki, J. Huang, C. Francis, S. Wang, G. Tarroni, F. Guitton, N. Aung, K. Fung, S. E. Petersen, S. K. Piechnik, S. Neubauer, E. Evangelou, A. Dehghan, D. P. O’Regan, M. R. Wilkins, Y. Guo, P. M. Matthews and D. Rueckert. A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine 26:1654–1662, 2020.

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

G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, I. D. L. Costa Junior, J. Mancuso, F. Jungmann, M.-M. Steinborn, A. Saleh, M. Makowski, D. Rueckert and R. Braren, End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, in press, 2021.

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Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
Damiano Baldoni - Poenia (Outro)

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