Medical Imaging with AI Integration (ELE670)
Medical data, in the form of signals and images, is largely used as an important part of the diagnostics. This course deals with some key techniques for collecting such data. The theme is seen in relation to signal and image processing as well as machine learning, which are core subjects in the study program, as such methods can be used for automatic segmentation, interpretation and analysis of signals and images. In modern diagnostics, automatic data analysis can be included as decision support.
The the following techniques will be emphasized: Electrocardiography (ECG), Electroencephalography (EEG), Ultrasound, X-ray, Magnetic Resonance Imaging (MR), Computer Tomography (CT), Angiography.
Course description for study year 2025-2026. Please note that changes may occur.
Course code
ELE670
Version
1
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Content
NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester.
This course addresses how some selected medical images are formed and the characteristics of these. This is, to some extent, seen in context with the themes and techniques of image processing and machine learning and artificial intelligence.
The course will focus on principles, modes of operation, applications, and study of example images for some common techniques for collecting medical diagnostic data. The following techniques will be highlighted:
- X-ray
- Magnetic Resonance Imaging (MR)
- Computer tomography (CT)
- Ultrasound
- Integration of AI in medical images, analysis and acquisition
Learning outcome
Knowledge:
The purpose of the course is to provide students with a technological background insight into techniques for the formation of medical diagnostic images and integration of AI applied to the data. Such medical data should then be seen in the context of techniques and knowledge from other subjects. Students will learn about a number of different techniques for collecting medical diagnostic data. The following will be emphasized: x-ray, magnetic resonance imaging (MRI), computer tomography (CT), ultrasound, angiography, microscopy etc and how to use AI for better data collection and analysis. Students will learn about the principles, operations and applications of these techniques, for example by means of sample images.
Skills:
The students should be able to explain the principles behind some techniques for collecting medical diagnostic signals and images. The student should be able to recognize and understand the meaning of specific characteristics from different types of images and how artificial intelligence is used to analyze them and what are its effects.
General competence:
After taking this course, students will be able to understand the connection between medical diagnostic images and physiological phenomena.
Required prerequisite knowledge
Recommended prerequisites
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Written exam | 1/1 | 4 Hours | Letter grades | None permitted |
Digital written exam in Inspera.