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.
- 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