Die Kosten für Tutorials sind bis zu 31.01.2018 reduziert und ab dem 01.02.2018 zu regulärem Preis weiter verfügbar.
Genaueres können Sie der Registrierungsseite entnehmen.
Der Anmeldeschluss für Tutorials ist der 17.02.2018.


Katharina Breininger, Vincent Christlein, Tobias Würfl, Andreas Maier
Pattern Recognition Lab, Friedrich-Alexander-Universität, Erlangen, Germany

Deep Learning Fundamentals

Deep learning has received a lot of attention in the machine learning community. Successful applications from speech recognition or computer vision are already part of our daily life. Much effort has been devoted to transferring this success to medical image computing. Therefore, neural networks have become an essential research direction.
The first half of this tutorial is designed to familiarize participants with neural networks. The second half presents the transition from neural networks to deep learning.
The building blocks of classical neural networks, such as the multi-layer perceptron, activations and loss functions, are explained. Furthermore, the concepts of gradient-based learning and backpropagation to calculate the gradients are introduced.
The second part of the tutorial covers the elements of convolutional neural networks, around which most successful deep learning applications revolve. Special attention is devoted to regularization techniques, which are essential to state-of-the-art performance. Best practices and exemplary architectures conclude the tutorial.

Paul Jäger, Fabian Isensee, Jens Petersen, David Zimmerer, Jakob Wasserthal, Klaus Maier-Hein
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

Advanced Deep Learning Methods

The remarkable rise of Deep Learning has led to an overwhelming amount of new papers coming up by the week. This tutorial intents to filter out the research most relevant for the medical image computing (MIC) community and present it in a structured and understandable form. It is composed of five parts: Classification, Segmentation, Detection, Generative Models and Semi- Supervised Learning. Each part starts off with a thorough motivation, shows exemplary use cases related to MIC, provides a brief model overview and describes the current state-of-the-art methods in the respective area.
Basic knowledge about Neural Networks as covered by the “Deep Learning Fundamentals” tutorial is recommended.

Michael Friebe
Professor of Catheter Technologies and Image Guided Therapies, OvGU
Dieses Tutorial findet aufgrund zu geringer Teilnehmerzahl leider nicht statt.

Innovation Generation, Disruption and Exponential Technologies in Medical Imaging

Healthcare — and the subset Medical Imaging — will change dramatically in the next 20 years with the emergence of artificial intelligence (including machine and deep learning), 3D printing, personalised diagnosis and therapies, shift from therapy to prevention, and many related delivery and economic changes.
Huge opportunities are present in developed, emerging and developing nations … each with completely different needs and infrastructural environments … and with a completely different reimbursement system.
The tutorial will prepare the attendees for the changes that will likely come up in the coming years and subsequently focus on recognising the need for a change in our development and innovation process, that must focus on digitisation and connectivity, small footprint, robustness, and low cost … and with that explore alternative financing methods.
Due to the regulatory processes in place and the ever increasing complexity of getting products into the market there could also be lots of opportunities for reverse innovation.
The lecture will also present the product and technology approach and some recent examples of the chair of catheter technologies and image guided therapies that focusses on joint product developments between clinicians and engineers leading to the question of what are good versus bad value propositions for Medical Technology/Imaging Products?
Following questions will be covered:

  • What actually is Innovation and can meaningful Innovation Generation for Healthcare products and services be learned?
  • What are good versus bad value propositions for Medical Technology / Imaging Products?
  • What likely impact will exponential technologies have on healthcare delivery / medical imaging in the next 20 years?
  • What entrepreneurial opportunities will develop because of that and how do we prepare ourselves for that?
  • What is reverse innovation and how can all benefit from that?

Thomas Wittenberg
Fraunhofer Institut IIS Erlangen
Dieses Tutorial findet aufgrund zu geringer Teilnehmerzahl leider nicht statt.


In the last 40 years progress in the field of diagnostic and interventional endoscopy (gastroenterology and laparoscopy) have been made, mainly driven through new technical developments from various areas of medical technologies such as optics, micro-mechanics, electronics, robotics, informatics, and image processing. These advances allow soft and gentle minimal-invasive access and interventions in all hollow organs, including the abdomen, stomach, large and small intestines, joints a was well nasal cavities.

To accelerate the development and application of such diagnostic and therapeutic possibilities in all fields of endoscopy a understanding of the available technological possibilities is necessary.
In this short tutorial, we will give an overview about the following topics:

• Brief history and introduction to endoscopes
• Components of endoscopy systems (Illumination, sensors, instruments, …)
• Types of endoscopes (rigid, flexible, capsule, stereo)
• Examples for clinical application of diagnostic and interventional endoscopy
• Endoscopy and robotic interventions
• Endoscopy based challenges for image processing
• Future developments

As part of the tutorial we will provide a hands-on demonstration for some endoscopy procedures including colonoscopy and laparoscopy training systems