- Feature extraction
- Image classification
- Image regression
- Machine learning and deep learning for image analysis
The course consists of lectures, laboratories, mathematical exercises, and an exam. Participants combine basic and advanced software libraries for image registration in Python, including scipy, numpy, SimpleITK, scikit-image, scikit-learn, TensorFlow, etc. The course also includes introductory labs for students with programming experience but no Python experience.
Image analysis is used to extract relevant information from images. Image analysis is important for the diagnosis and treatment of various diseases. The course covers concepts, theories, and the most used methods in image analysis. The course is focused on solving medically relevant problems.
After completing the course, the participant should be able to:
- Understand the main problems and challenges in image analysis
- Describe the main principles and methods and the main differences between them
- Summarize the advantages and disadvantages and scope of different methods
- Identify and understand the mathematical theory behind the most used methods
- Develop and systematically evaluate different methods for solving simplified problems
- Analyze the effect of different parameters of the methods in particular situations
- Explain the proposed strategy for solving specific problems
in order to:
- understand the complete workflow for using computational tools for image analysis in a medical context
- be able to implement computational solutions in image analysis to medically relevant problems
- have a broad knowledge base that can facilitate understanding literature in the field