Marriott Marquis Houston
Houston, Texas, United States
15 - 20 February 2020
Course (SC1235)
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Introduction to Medical Image Analysis Using Convolutional Neural Networks
  • Instructors:
  • Markus Thorsten Wenzel, Fraunhofer Institute for Digital Medicine MEVIS (Germany);
  • Florian Weiler, Fraunhofer Institute for Digital Medicine MEVIS (Germany);
Saturday 15 February 2020
8:30 AM - 5:30 PM
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Member Price $595.00
Non-Member Price $690.00
Student Member Price $306.00
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Course
Details
  • Course Level:
  • Introductory
  • CEU:
  • 0.7
Course
Summary
Segmentation, detection, and classification are major tasks in medical image analysis and image understanding. Medical imaging researchers heavily use the results of recent developments in machine learning approaches, and with deep learning methods they achieve significantly better results in many real-world problems compared to previous solutions. The course aims to enable students and professionals to apply deep learning methods to their data and problem. Using an interactive programming environment, participants of the course will explore all required steps in practice and learn the tools and techniques from data preparation to result interpretation. We will work on example data and train models to segment anatomical structures, to detect abnormalities, and to classify them. Simple methods to explain predictions and assess network uncertainty will be discussed briefly as well. Participants will work in a prepared online environment providing selected deep learning toolkit installations, example data, and fully functional skeleton code as a basis for own experiments.
Learning Outcomes
  • describe the state of the art of deep learning methods in medical applications
  • construct computing pipeline using Python based infrastructure, using frameworks (Keras, Tensorflow) commonly used for research
  • select a suitable deep learning network architecture for a given problem and implement it
  • explain and interpret learning progress using appropriate metrics
  • interpret the resulting model performance using simple visual analytics
Intended
Audience
Students, researchers, and engineers from academia and industry, who seek to obtain first practical working knowledge in deep learning.
About the
Instructors
Markus Thorsten Wenzel works on machine learning methods for medical applications since 2005 and has published more than 30 conference and journal papers on the subject. He received his PhD for his work on decision support systems for breast care. At Fraunhofer MEVIS, he is a senior scientist for cognitive medical computing. He is a funded member of the Fraunhofer Society research class "Cognitive Machines" and is experienced in teaching and lecturing for academia and industry. He has acquired and led several international research projects.
Florian Weiler has been working on the field of medical image processing since 2006. His research interests focus on both image-analysis and computer-visualization of medical imaging data, with a special interest in clinical applicability. He received his PhD in the field of neuro-image analysis, but has previously also been active in the context of liver-surgery planning and software support for radiation therapy. At Fraunhofer MEVIS, he currently works in the decision-support team, with a focus on deep-learning based methods for predicting therapy response in neurologic diseases.
Additional
Notes
This is an interactive course and participants will need to bring their own laptops.
Attendee testimonial:
Great presentation, great hands-on material very knowledgeable instructors.
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