Postgraduate taught 

Digital Cancer Technologies MSc

Digital Pathology and Image Analysis BIOL5407

Digital Pathology and Image Analysis BIOL5407

  • Academic Session: 2023-24
  • School: School of Cancer Sciences
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No

Short Description

This course will equip students with knowledge in basic tissue pathology in cancer, tissue histopathology techniques and governance in human tissue research. Student will learn how to identify tumour cell structures and to understand the theory behind various pathology techniques. They will also learn about different model systems and how they are utilized in research.


Teaching takes place over 5 weeks in Semester 1 with students attending lectures, seminars, practical skills workshops and tutorials.

Excluded Courses





1. Critical review (2000 words) (75% weighting). ILO1 - 4

2. Oral group presentation (25% weighting) ILO1, 2

Course Aims

The aim of this course is to:

  • Understand core areas of digital pathology and image analysis including machine learning, WSI, file formats and running analysis.
  • Acquire knowledge in the applications and limitations of different digital pathology platforms
  • Understand regulations surrounding use of digital pathology platforms

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Critically evaluate the uses and limitations of tissue slide scanning platforms.

2. Critically evaluate the uses and limitations of digital pathology software.

3. Demonstrate critical understanding of digital pathology and image analysis software including viewing, annotating, and identifying relevant tissue.

4. Critically discuss regulations surrounding GDPR and ethical considerations in digital pathology.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course’s summative assessment.