Module CS3831-KP04

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

4 (Typ B)

Course of studies, specific fields and terms:

• Bachelor MES 2020, optional subject, medical engineering science

Classes and lectures:

• Programming for machine learning and image processing in medicine (exercise, 2 SWS)
• Programming for machine learning and image processing in medicine (lecture, 1 SWS)

Workload:

• 45 hours in-classroom work
• 75 hours private studies

Contents of teaching:

• Introduction to C++ programming for medical image processing
• Basic data structures for medical data (arrays, lists)
• Branches, loops, functions, arguments and recursion
• Classes and Objects
• Efficient and parallel programming for medical image data
• Use of the Eigen- and LibTorch program libraries
• Implementation of filters for medical image processing
• Medical image analysis with machine learning in pytorch
• Convolutional filters and neural network classifiers
• Implementation of prototype algorithms in python

Qualification-goals/Competencies:

• Students have a good overview of the special requirements of medical image processing for programming.
• They have an understanding of the basics of object-oriented programming.
• They will acquire the skills to integrate and use external libraries.
• They learn good C++ and python knowledge.
• They are enabled to design, implement and test programs independently.
• They will learn to implement new informatics or mathematical methods in solutions for practical applications in medical image processing with machine learning.

Grading through:

• continuous, successful participation in practical course

Responsible for this module:

• Prof. Dr. Mattias Heinrich

Teacher:

• Institute of Medical Informatics
• Prof. Dr. Mattias Heinrich

Literature:

• Lippman : C++ Primer Addison-Wesley Longman, Amsterdam

Language:

• German and English skills required

Notes:

Last Updated:

05.09.2021