Location: Zoom

Jan 20 2021

Bio: Since 2020 Daniel Rueckert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich. He is also Professor of Visual Information Processing in the Department of Computing at Imperial College London where he served as Head of the Department of Computing. He has gained a MSc from Technical University Berlin and a PhD from Imperial College. His research interests cover the area of biomedical image computing covering all aspects from image acquisition to image analysis and interpretation. He is also interested in developing novel AI and machine learning algorithms for the extraction of clinically useful information from medical images. He has published more than 500 journal and conference articles in this area. He is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing, MICCAI/Elsevier Book Series, and a referee for a number of international medical imaging journals and conferences. He has served as a member of organising and programme committees at numerous conferences. In 2014, he has been elected as a Fellow of the MICCAI society and in 2015 he was elected as a Fellow of the Royal Academy of Engineering and as fellow of the IEEE. More recently he has been elected as Fellow of the Academy of Medical Sciences (2019).

The talk will focus on the use of deep learning techniques for the discovery and quantification of clinically useful information from medical images. The talk will describe how deep learning can be used for the reconstruction of medical images from undersampled data, image super-resolution, image segmentation and image classification. It will also show the clinical utility of applications of deep learning for the interpretation of medical images in applications such as brain tumour segmentation, cardiac image analysis and applications in neonatal and fetal imaging. Finally, it will be discussed how deep learning may change the future of medical imaging.

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