26 Nov: Advances in Medical Deep Learning – Virtual Fall Meeting Session 3 of 3
Instead of our regular Fall meeting this year we present three short virtual events in November 2020.
At each event two keynote lectures are presented with a short moderated Q&A session.
Our six renowned speakers will show you how advances in deep learning are applied to medical imaging.
This last session we have invited Dr. Nicola Pezzotti and ”, Dr. Hugo Kuijf for a keynote
On Tuesday 17th, Tuesday 24th and Thursday 26th from 15:30 to 17:00, Bart ter Haar Romeny and John Schavemaker will be your hosts.
All session are free after registration. You only need to supply your own drinks.
- 15:30 - 15:35
Speakers:Em.Prof. Bart ter Haar Romeny
- 15:35 - 16:20
- Deep Learning to Boost Medical Systems’ Performance
- Keynote with Q&A
Speakers:Dr. Nicola Pezzotti
- 16:20 - 17:00
- Machine Learning for the Detection of Brain Abnormalities
- Keynote with Q&A
Speakers:Dr. Hugo Kuijf
Dr. Hugo KuijfImage Sciences Institute, UMC Utrecht
Hugo Kuijf is assistant professor at the Image Sciences Institute, UMC Utrecht; programme coordinator of the MSc programme Medical Imaging; member of the Board of Examiners of the Graduate School of Life Sciences; chair of the Education Committee of the PhD programme Medical Imaging; and university lecturer at Eindhoven University of Technology.
Hugo Kuijf graduated in Computer Science at Utrecht University in 2009, with academic minors in Software Engineering and Game- and Media Technology. In 2013, he received his PhD in Medical Imaging after defending his thesis entitled “Image processing techniques for quantification and assessment of brain MRI”.
His research focuses on innovative image processing and (deep) machine learning techniques for the quantification and assessment of brain MR images. These techniques are applied in the context of brain anatomy and pathology, in particular small vessel disease. Semi-automated techniques for the detection of microbleeds, microinfarcts, small arteries and veins, perivascular spaces, and white matter hyperintensities are developed; including lesion-symptom mapping solutions. Development and utilization of modern machine learning and deep learning techniques (also known as “artificial intelligence”) are a central pillar in the development of new medical image analysis techniques.
He organized the MICCAI grand challenges on WMH segmentation and brain tissue segmentation (MRBrainS13 and MRBrainS18). He developed freely available software for the detection of the midsagittal plane and surface and lesion-symptom mapping.
Dr. Nicola PezzottiPhilips Research and TU Eindhoven
Nicola Pezzotti is a Senior Scientist in Artificial Intelligence at Philips Research, Eindhoven, the Netherlands, focusing on the development of reliable and human-centered Artificial-Intelligence solutions for healthcare. He also has a joint appointment as assistant professor at Eindhoven University of Technology, focusing on human-centered AI. His research interests include machine learning, medical imaging, visual analytics, explainable AI, optimization techniques and software engineering. He received his BSc and MSc degrees in Computer Science and Engineering from the University of Brescia, Italy, in 2009 and 2011. He received his PhD cum Laude from Delft University of Technology, the Netherlands, in 2018. Besides his experience in the startup world, he was a visiting scientist to INRIA Saclay, Paris, in 2017 and Google AI, Zurich, in 2018. He is recipient of several awards, including the IEEE VGTC Best Dissertation Award, TU Delft Excellence in Research and the Dirk Bartz Prize for Visual Computing in Medicine.
Nicola worked at Google AI with Alexander Mordvintsev, the creator of Google’s DeepDream. At Google, he published two research papers and developed an open source library released in the TensorFlow.js family (https://github.com/tensorflow/tfjs-tsne).