RDGY30440 Introduction to medical image analysis and machine learning

Academic Year 2022/2023

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.

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Curricular information is subject to change

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
Lab Report: Detailed record of practical sessions following the assignment guidelines Throughout the Trimester n/a Graded No

50

Presentation: Presentation based on theoretical content and personal research Throughout the Trimester n/a Graded No

50


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
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
     
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33 Mon 12:00 - 12:50
Practical Offering 1 Week(s) - 20, 22, 23, 24, 26, 30, 32 Wed 16:00 - 17:50
Spring