Donnerstags-Fortbildung: Second Zurich Machine Intelligence in Clinical Neuroscience Symposium

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Machine Vision: Perspectives on the Future of Perception



University Hospital Zurich
Auditorium Monakow (HAL A 34)




9.00 am - 5.20 pm UTC +1 (Zurich)




Free of charge


9.00 amWelcome
Prof. Luca Regli, Dr. Carlo Serra, MICN Lab, Neurosurgery University Hospital Zurich (CH)
Session I: Basics
Session Chair: Prof. Victor X.D. Yang (Toronto, ON, Canada)
Moderators: Victor Staartjes, Dr. Carlo Serra
9.10 amIntroduction to Supervised and Unsupervised Learning
Prof. Ingo Scholtes, Chair of Machine Learning for Complex Networks, University of Würzburg (DE), University of Zurich (CH)
9.40 amHandling Neuroimaging Data for Machine Learning
Dr. Antonios Thanellas, Helsinki University Hospital (FI)
10.00 amUnsupervised Pattern Discovery in Large-Scale Imaging Data
Dr. Julius Kernbach, RWTH Aachen University Hospital (DE)
10.20 amComputational Fractal-Based Analysis and Radiomics of Brain Tumors
Prof. Antonio Di Ieva, Computational NeuroSurgery (CNS) Lab, Macquarie University (AU)
10.40 amMachine Vision for Wildlife Surveillance in Namibia
Marc Ungeheuer, Wild Intelligence Lab; Christopher Rösel, RWTH Aachen University (DE); Dr. Friedrich Reinhardt, Kuzikus Reserve (NAM)
11.10 amKeynote Session: Current Applications of Machine Vision
Prof. Tom Vercauteren, King’s College London (UK)
12.00 noonLunch Break
Session II: Machine Vision
Session Chair: Prof. Ender Konukoglu (Zurich, Switzerland)
Moderators: Dr. Carlo Serra, Victor Staartjes
1.00 pmAutonomous Driving with Motorcycles
Dr. Simon Hecker, Aegis Rider (CH)
1.20 pmSpoofing Attack Detection
Dr. Suman Saha, Computer Vision Lab (CVL), ETH Zurich (CH)
1.40 pmLeveraging Federated Learning to Overcome the Challenges of Developing AI in Healthcare
Dr. Colin Compas, NVIDIA Corporation (USA)
2.00 pmKeynote Session: History and Evolution of Machine Vision in Medical Imaging
Prof. Ron Kikinis, Harvard Medical School (USA)
2.50 pmLunch Break
Session III: Clinical Applications of Machine Learning
Session Chair: Prof. Luca Regli (Zurich, Switzerland)
Moderators: Dr. Carlo Serra
3.30 pmAutomated Intraoperative Anatomical Recognition
Victor Staartjes, MICN Laboratory, Neurosurgery, University Hospital Zurich (CH)
3.50 pmMachine Learning in Pituitary Surgery
Prof. Gabriel Zada, University of Southern California (USA)
4.20 pmCombining Machine Learning and Augmented Reality
Prof. Tristan van Doormaal, Neurosurgery
University Hospital Zurich (CH)
4.40 pmHyperspectral imaging and machine learning during tumor surgery
Dr. Gustav Burström, Clinical Neuroscience, Karolinska Institute (SE)
5.10 pmClosing Remarks
Prof. Luca Regli, Dr. Carlo Serra, MICN Lab, Neurosurgery University Hospital Zurich (CH)


Luca Regli


Ender Konukoglu, Victor Staartjes, Carlo Serra
Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery & Clinical Neuroscience Center, University Hospital Zurich, University of Zurich

Invited Faculty

  • Ron Kikinis (Boston, MA, USA)
  • Tom Vercauteren (London, UK)
  • Gabriel Zada (Los Angeles, CA, USA)
  • Ingo Scholtes (Zurich, CH)
  • Victor Yang (Toronto, ON, USA)
  • Antonio Di Ieva (Sydney, AU)
  • Suman Saha (Zurich, CH)
  • Colin Compas (Nashville, TN, USA)
  • Simon Hecker (Zurich, CH)
  • Gustav Burström (Stockholm, SE)
  • Antonios Thanellas (Helsinki, FI)
  • Tristan van Doormaal (Zurich, CH)
  • Julius Kernbach (Aachen, DE)
  • Christopher Rösel (Aachen, DE)
  • Marc Ungeheuer (Aachen, DE)
  • Friedrich Reinhardt (Kuzikus, NAM)

Registration & Participation

The event will be held in a hybrid format: Online over Zoom and at the Monakow Auditorium at the University Hospital Zurich. You will receive the Zoom link and further information in time and after registering using the abovementioned form. On-site participation is restricted to employees of the USZ, UZH, and ETH, as well as invited guests. A recording of the symposium will be made available on the MICN Lab website.

Register now


Igea Cesarano

Tel. +41 44 255 88 19

Verantwortlicher Fachbereich

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