Fraunhofer MEVIS presents itself at the SPIE Medical Imaging 2020
Computer-aided diagnosis, artificial intelligence and navigation of vascular catheters – these are the topics Fraunhofer MEVIS is presenting at the “Medical Imaging” conference of the International Society for Optics and Photonics (SPIE), which will take place in Houston, Texas, from February 15 to 20. The Fraunhofer Institute for Digital Medicine MEVIS is again represented at the renowned conference with several contributions.
Institute Director Horst Hahn is co-chairing the conference “Computer-Aided Diagnosis” together with Maciej Mazurowski from Duke University in Durham, North Carolina. The four-day conference is structured into a total of 13 sessions and deals, among other things, with computer-aided detection of breast and lung cancer as well as orthopedic and neurological diseases. The use of artificial intelligence methods is becoming increasingly important. Professor Hahn himself chairs two of the 13 sessions (“Mammography”, “Keynote and Methodology”).
MEVIS researchers Markus Wenzel and Florian Weiler offer a full-day workshop on February 15 on the topic “Introduction to Medical Image Analysis Using Convolutional Neural Networks” and a half-day advanced course on “Adversarial Networks: From Architecture to Practical Training“ on February 16. The workshops are aimed at students, scientists and engineers from academia and industry who want to gain first practical work experience on the topic of “Deep Learning”, a variant of machine learning.
In addition, scientists from Fraunhofer MEVIS will give the following presentations at SPIE Medical Imaging:
- Sonja Jäckle: „3D catheter guidance including shape sensing for endovascular navigation“ (16. Februar, 8:00, Hunters Creek)
+++ Awarded with the runner-up of the Image-Guided Procedures, Robotic Interventions, and Modeling Student Paper Award +++
- Markus Wenzel: „QuantMed: Component-based deep learning platform for translational research“ (17. Februar, 11:10, Salon River Oaks)
- Horst Hahn: „Feasibility of end-to-end trainable two-stage U-Net for detection of axillary lymph nodes in contrast-enhanced CT based on sparse annotations“ (18. Februar, 10:30, Salon B)
- Sven Kuckertz: „Deep learning based CT-CBCT image registration for adaptive radio therapy“ (18. Februar, 16:10, Salon C)