Welcome back to Beyond the Patterns.
So today I have the great pleasure to introduce Professor Dr. Daniel Rueckert in our seminar
series.
So he is a researcher at the TU Munich and Professor Rueckert's field of research is
in the area of artificial intelligence and machine learning and their applications to
medicine and healthcare.
His research focuses on the development of innovative algorithms for biomedical image
acquisition, image analysis and image interpretation, especially in the areas of image reconstruction,
registration, segmentation and modeling.
Also he is researching on AI for extracting clinically useful information from biomedical
images, especially for computer assisted diagnosis and prognosis.
Since 2020, Daniel Rueckert is Alexander from 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 Masters from Technical University in Berlin in 1993, a PhD from Imperial College
in 1997, followed by a postdoc 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 a head of the Department of Computing at Imperial College.
So today it's a great pleasure to announce him here and the talk will be entitled AI
and the future of Radiology.
Daniel, it's a great pleasure to have you here and the stage is yours.
Thank you very much Andreas, thanks very much for the kind invitation.
I'm very glad to be able to give you a presentation today about some of the ideas we have for
how AI might change the future of radiology.
The title is perhaps a bit provocative in terms of that it's going to change a lot
the future of radiology and perhaps we'll have to take some baby steps before we actually
get to the revolution in radiology and in the field of medicine in general.
And so what I wanted to do today is give you a broad overview of some of the work we are
interested in our group in Munich and at Imperial College in London and I think it's very similar
to the group to the interests of Andreas' group and things you do in Erlang and I also
hope that we will be able to collaborate on some of the topics of mutual interest in the
future.
But I just wanted to also highlight a few newspaper headlines which some of you might
have seen in the last couple of months and years about how AI might change the field
of medicine in general and you can already see from these headlines some of them are
relatively provocative in terms of how they portray the future of medicine in the age
of AI.
Now I'm going to talk more specifically about the field of radiology because that's the
field I've been working on for a number of years and also where I think AI in its current
form which is I guess largely based on machine learning and deep learning has probably had
the biggest impact so far because I think quite a lot of the tasks which occur in this
area are perception driven tasks and that's where actually a lot of deep learning really
has been extremely successful.
Now the other reason is really that if you look at for example the FDA, the Federal Drug
Administration in the US which effectively regulates medical devices and AI algorithms
are such medical devices then you also see that almost half of all the technologies in
this space of AI really focus on radiology.
So radiology seems to be sort of at the forefront of adopting AI.
Now it's not only at the forefront but it also has led a number of people to make quite
Presenters
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01:12:36 Min
Aufnahmedatum
2021-07-22
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2021-07-22 10:26:05
Sprache
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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)