Show/hide contentOpenClose All
Curricular information is subject to change
After completion of this module, the student will be able to:
1. Solve problems at the interface of computer science, imaging and medicine.
2. Explain how digital images are represented, manipulated and processed.
3. Apply advanced image processing algorithms to medical images to derive meaningful information.
4. Apply supervised and unsupervised machine learning techniques to segment and classify medical images.
5. Develop, validate and interpret AI models to gain insight into disease as diagnosed by medical imaging.
Advanced AI applications will include XAI or explainable AI; multi-modality: MRI classification with multi-modal inputs, e.g. from another imaging modality; transfer-learning: learn features on large datasets and transfer them to different, smaller datasets.
|Student Effort Type||Hours|
|Autonomous Student Learning||
|Computer Aided Lab||
Not applicable to this module.
|Description||Timing||Component Scale||% of Final Grade|
|Lab Report: Detailed record of practical sessions following the assignment guidelines.||Throughout the Trimester||n/a||Graded||Yes||
|Presentation: Presentation based on theoretical content and personal research||Throughout the Trimester||n/a||Graded||Yes||
|Resit In||Terminal Exam|
• Group/class feedback, post-assessment
Feedback will be provided based on an analysis of general weaknesses and strong points
|Dr John Healy||Lecturer / Co-Lecturer|
|Misgina Tsighe Hagos||Tutor|
|Ms Katie Noonan||Tutor|