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RDGY41710

Academic Year 2024/2025

AI For Medical Image Analysis (RDGY41710)

Subject:
Radiography
College:
Health & Agricultural Sciences
School:
Medicine
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Assoc Professor Kathleen Curran
Trimester:
Summer
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides a foundation in image processing and machine learning with a focus on deep learning. Students will learn how to apply advanced AI techniques in the study of radiological diagnosis. Lectures will introduce the concepts of basic and advanced image analysis and machine learning computational techniques as applied in medical imaging. The module gives an overview of analysis pipelines’ used for diagnostic imaging and techniques that can be applied in the areas of image enhancement, region of interest definition, filtration, segmentation and image registration. 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.

About this Module

Learning Outcomes:

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.

Indicative Module Content:

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 Hours:
Student Effort Type Hours
Autonomous Student Learning

160
Lectures

30
Computer Aided Lab

10
Total

200

Approaches to Teaching and Learning:
Active/task-based learning; peer and group work; lectures; critical writing; reflective learning; lab work; enquiry & problem-based learning; debates; student presentations.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback will be provided based on an analysis of general weaknesses and strong points

Name Role
Dr John Healy Lecturer / Co-Lecturer
Niamh Belton Tutor
Ms Katie Noonan Tutor
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