RDGY41710 AI For Medical Image Analysis

Academic Year 2023/2024

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.

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Curricular information is subject to change

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
Lectures

30
Computer Aided Lab

10
Autonomous Student Learning

160
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Lab Report: Detailed record of practical sessions following the assignment guidelines. Throughout the Trimester n/a Graded Yes

50
Presentation: Presentation based on theoretical content and personal research Throughout the Trimester n/a Graded Yes

50

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
Mr Carles Garcia-Cabrera Tutor
Misgina Tsighe Hagos Tutor
Siteng Ma Tutor
Ms Katie Noonan Tutor
Fangyijie Wang Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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