Prof. Dr. Bram van Ginneken

Diagnostic Image Analysis Group Radboud University Medical Center, Nijmegen

How Deep Learning Has Transformed Medical Image Analysis

Deep learning or, more specifically, deep convolutional networks is currently the most powerful tech-nology for image analysis. In this talk, I will illustrate this with recent results and I will emphasize the importance of medical image analysis challenges. In these challenges, the research community at large is invited to participate in developing a solution for a specific task. This paradigm is very successful and combines well with the ongoing move towards open data, open source software, and open science.


Bram van Ginneken is Professor of Medical Image Analysis at Radboud University Medical Center and chairs the Diagnostic Image Analysis Group. He also works for Fraunhofer MEVIS in Bremen, Germany, and is a founder of Thirona, a company that develops software and provides services for medical image analysis. He studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he obtained his Ph.D. at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography. He has (co-)authored over 200 publications in international journals. He is Associate Editor of IEEE Transactions on Medical Imaging and member of the Editorial Board of Medical Image Analysis. He pioneered the concept of challenges in medical image analysis.

Prof. Dr. Fabian Bamberg

Klinik für Diagnostische und Interventionelle Radiologie Universitätsklinikum Freiburg

Radiomics, Machine Learning and Artificial Intelligence in Radiology:
Clinical Benefits?

From a clinical perspective, advanced image post-processing has been perceived ambivalently. In the beginning practicing Radiologists were frightened by the mere power of AI and deep learning as their job appeared to be jeopardized. Over time, the potential of these techniques is seen more of an attractive opportunity to better handle every day’s work load. The talk will provide an overview of current challenges that clinical Radiology is facing and explore the potential of advanced post-processing techniques for improved clinical decision-making. Results of early implementations and limitations of current approaches will be discussed.


Prof. Bamberg was most recently Deputy Medical Director of Diagnostic and Interventional Radiology at the University Hospital of Tübingen. He studied medicine in Hanover, Witten-Herdecke and Harvard University and worked clinically at the Massachusetts General Hospital in Boston and at the Ludwig Maximilian University Hospital in Munich. In his research, the radiologist concentrates on the efficient use of modern imaging techniques in diagnostics and their influence on further treatment.

Prof. Dr. Daniel Cremers

Department of Computer Science Technische Universität München

Direct Methods for Camera-based 3D Reconstruction and Visual SLAM

The reconstruction of the 3D world from images is among the central
challenges in computer vision. Starting in the 2000s, researchers have
pioneered algorithms which can reconstruct camera motion and sparse
feature-points in real-time. In my talk, I will introduce direct
methods for camera tracking and 3D reconstruction which do not require
feature point estimation, which exploit all available input data and
which recover dense or semi-dense geometry rather than sparse point
clouds. They lead to a drastic boost in precision and
robustness. Furthermore, I will showcase some applications ranging
from 3D photography and 3D television to autonomous navigation.


Daniel Cremers received Bachelor degrees in Mathematics (1994) and Physics (1994), and a Master’s degree in Theoretical Physics (1997) from the University of Heidelberg. In 2002 he obtained a PhD in Computer Science from the University of Mannheim, Germany. Subsequently he spent two years as a postdoctoral researcher at the University of California at Los Angeles (UCLA) and one year as a permanent researcher at Siemens Corporate Research in Princeton, NJ.

From 2005 until 2009 he was associate professor at the University of Bonn, Germany. Since 2009 he holds the chair for Computer Vision and Pattern Recognition at the Technical University, Munich. His publications received several awards, including the ‚Best Paper of the Year 2003‘ (Int. Pattern Recognition Society), the ‚Olympus Award 2004‘ (German Soc. for Pattern Recognition) and the ‚2005 UCLA Chancellor’s Award for Postdoctoral Research‘. For pioneering research he received a Starting Grant (2009), a Proof of Concept Grant (2014) and a Consolidator Grant (2015) by the European Research Council.

Professor Cremers has served as associate editor for several journals including the International Journal of Computer Vision, the IEEE Transactions on Pattern Analysis and Machine Intelligence and the SIAM Journal of Imaging Sciences. He has served as area chair (associate editor) for ICCV, ECCV, CVPR, ACCV, IROS, etc, and as program chair for ACCV 2014. In 2018 he organized the largest ever European Conference on Computer Vision in Munich with 3300 delegates. He is honorary member of the Dagstuhl Scientific Directorate. In December 2010 he was listed among „Germany’s top 40 researchers below 40“ (Capital). On March 1st 2016, Prof. Cremers received the Gottfried Wilhelm Leibniz Award, the biggest award in German academia. According to Google Scholar, Prof. Cremers has an h-index of 80 and his papers have been cited 26672 times. According to Guide2Research he is among the most influential scientists in Germany. He is co-founder of several companies, most recently the high-tech startup Artisense.