24 Nov: Advances in Medical Deep Learning – Virtual Fall Meeting Session 2 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 second session we have invited Prof. dr. Ivana Išgum and Dr. Ioannis Giotis 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.
Hourly Schedule
Program
- 15:30 - 15:35
- Opening
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Speakers:
Em.Prof. Bart ter Haar Romeny
- 15:35 - 16:20
- Image Analysis in the AMC Quantitative Medical Image Analysis (QIA) group
- Keynote with Q&A
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Speakers:
Prof. dr. Ivana Išgum
- 16:20 - 17:00
- Next Move in Movement Disorders (NEMO)
- Keynote with Q&A
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Speakers:
Dr. Ioannis Giotis
Speakers
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Prof. dr. Ivana IšgumAMC Amsterdam
Ivana Išgum is a University Professor of AI and Medical Imaging at the Amsterdam University Medical Center, University of Amsterdam. In fall 2018 she started as Scientific Lead of the company Quantib-U. Ivana Išgum graduated in Mathematics at the University of Zagreb, Croatia in 1999. She obtained her PhD degree at the Image Sciences Institute in 2007 with a thesis titled ‘Computer-aided detection and quantification of arterial calcifications with CT’. She was a postdoc at the Laboratory for Clinical and Experimental Image Processing in Leiden University Medical Center, and subsequently Assistant and Associate professor at UMC Utrecht where she was leading Quantitative Medical Image Analysis (QIA) group at the Image Sciences Institute. In 2019, Išgum was appointed full professor at the University of Amsterdam and moved with her group to Amsterdam UMC – location AMC. Her group is focusing on the development of algorithms for quantitative analysis of medical images to enable automatic patient risk profiling, diagnosis and prognosis using AI techniques. Besides research, Išgum was involved in the creation of a national teaching program for PhD researchers in the field of medical image analysis and organized a summer course for Dutch PhD researchers in that same field.
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Dr. Ioannis GiotisZiuZ / RUG
Dr. Ioannis Giotis is R&D Engineer at ZiuZ Visual Intelligence, specialized in pattern recognition, machine learning, computer vision and data analysis. He received his MSc degree of the University of Piraeus (2008) and his PhD degree (2012) at Groningen University on a thesis titled “Advances in prototype-based learning and applications in dermatology”. He co-developed MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images.