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RDGY30440

Academic Year 2023/2024

Introduction to medical image analysis and machine learning (RDGY30440)

Subject:
Radiography
College:
Health & Agricultural Sciences
School:
Medicine
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Kathleen Curran
Trimester:
Spring
Mode of Delivery:
Online
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 analysis, and describes the computational tools that can be applied in the study of radiological images. It gives an overview of analysis possibilities used for diagnostic imaging and techniques that can be applied in the areas of image enhancement, background subtraction, region of interest definition, filtration, segmentation and image registration.

About this Module

Learning Outcomes:

On completion of this module, students 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 fundamental image processing algorithms to medical images to derive meaningful information. 4. Understand the complete image processing pipeline.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

40
Autonomous Student Learning

60
Lectures

10
Tutorial

1
Practical

10
Total

121

Approaches to Teaching and Learning:
Active task-based learning; peer learning; lectures; lab work; critical writing; reflective writing

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 In Module Component Repeat Offered
Lab Report: Detailed record of practical sessions following the assignment guidelines Throughout the Trimester n/a Graded No
50
 Yes
Presentation: Presentation based on theoretical content and personal research Throughout the Trimester n/a Graded No
50
 Yes

Carry forward of passed components
Yes
 

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

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

Peer feedback will be provided initially during group work. Group/class feedback will be provided post-assessment and this will later be followed up with individual feedback.

Name Role
Mr Patrick Leydon Tutor
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