sitem-insel AG

M4 - Applied AI in Medical Imaging

Module leaders: Dr. Richard McKinley
Prof. Dr. Hendrik von Tengg-Kobligk
Prof. Dr.-Ing. Kuangyu Shi

Date and Credits

Start: 11.02.2021

Duration: 6 Weeks


Participants will learn about the relationship between a clinical problem and a machine learning problem as well as the influence of training data quality and annotation errors on the ability of algorithms to learn. Participants will be introduced to state-of-the-art AI models and learn how to apply and validate them in practice.

Main learning objectives

  • Understand the formulation of a clinical problem as a machine-learning problem
  • Understand the challenges and practicalities of assembling a training set of medical images

  • Understand the tradeoffs between manual or automatic labelling of training data

  • Understand how to select a state-of the art model, and have hands-on experience training such a model

  • Understand and have hands on experience with selecting and applying appropriate metrics for the performance of a medical AI system

  • Understand how the results of validating a machine-learning model may affect design choices in a subsequent experiment

Learning content

  • Introduction to image classifiers, ChexNet dataset, image segmentation and Decathlon datasets
  • Labeling of image data, automatic vs. manual methods
  • Adapting existing code to a new problem: classification
  • Modern CNN architectures explained
  • Adapting code to a new problem: segmentation
  • Deep Learning Classifiers versus old fashioned modelling
  • Interpreting model performance
  • Issues in adapting code, 3D data, multimodal data, modern deep learning