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Unit of study_

MRTY5210: Artificial Intelligence in Medical Imaging

Semester 1, 2024 [Online] - Camperdown/Darlington, Sydney

This unit explores the state of the art and prospects for artificial intelligence applications in medical imaging, including considerations of technique optimisation, data analysis, validation, ethics, and regulation.

Unit details and rules

Unit code MRTY5210
Academic unit Clinical Imaging
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
None
Assumed knowledge
? 

Background in medical imaging or medicine

Available to study abroad and exchange students

No

Teaching staff

Coordinator Ziba Gandomkar, ziba.gandomkar@sydney.edu.au
Type Description Weight Due Length
Assignment Article proposal
Proposing a topic related to one of the areas, where AI impacts Med Imaging
10% Week 03
Due date: 10 Mar 2024 at 23:59
300-500 words
Outcomes assessed: LO2
Assignment Peer review
review 2 other students' initial proposal
10% Week 05
Due date: 24 Mar 2024 at 23:59
500 words
Outcomes assessed: LO2
Assignment Methodological review
Conduct the methodological review of the selected papers
40% Week 10
Due date: 05 May 2024 at 23:59
500-1000 words
Outcomes assessed: LO1 LO2 LO3
Assignment Critically review the selected papers
Identify the potential gaps for the translation of the solution in practice
40% Week 13
Due date: 26 May 2024 at 23:59
1000 words
Outcomes assessed: LO1 LO2 LO3

Assessment summary

The student is tasked with writing a 2500-3000 word article that critically evaluates a selection of papers on a topic of their choice. The task of writing the article will be split into three assignments (Introduction in Assessment 1, Methods Review in Assessment 3, and Discussion in Assessment 4) to provide the student with feedback. Students are encouraged to choose a topic based on their area of expertise.To familiarize the students with the grading criteria, they will be required to review two other students' initial proposals in Assessment 2. This will provide an opportunity for early engagement with the grading rubric and offer feedback on the critical reflection.

Assessment criteria

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Fail

0 - 49

When you do not meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

(a) For work submitted after the deadline but up to three calendar days late, a penalty of 15 per cent of the maximum mark awardable for the assignment will apply. (b) For work submitted after 3 days and less than one week after the deadline, a penalty of 30 per cent of the maximum mark awardable for the assignment will apply. (c) For work submitted more than one week late but less than two weeks after the deadline, a penalty of 50 per cent of the maximum mark awardable for the assignment will apply. (d) Work submitted more than two weeks after deadline will not be assessed (zero mark).

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Brief overview AI in medical imaging Online class (2 hr) LO1 LO2
Week 02 Basic principles of machine learning Online class (2 hr) LO1
Week 03 Basic principles of machine learning models for image analysis Online class (2 hr) LO1 LO2
Week 04 Basic principles of deep learning Online class (2 hr) LO1 LO2
Week 05 Underpinning technologies and architectures for deep learning Online class (2 hr) LO1 LO2
Week 06 Data required for AI development and AI Validation Online class (2 hr) LO1 LO2 LO3
Week 07 AI applications in medical imaging: Image interpretation Online class (2 hr) LO2 LO3
Week 08 AI applications in medical imaging: Image acquisition Online class (2 hr) LO2 LO3
Week 09 AI applications in medical imaging: Image segmentation Online class (2 hr) LO2 LO3
Week 10 AI applications in medical imaging: Image registration Online class (2 hr) LO2 LO3
Week 11 AI applications in medical imaging: Radiotherapy planning Online class (2 hr) LO2 LO3
Week 12 AI for Health Services management Online class (2 hr) LO2 LO3
Week 13 Unsupervised learning and AI for knowledge extraction Online class (2 hr) LO2 LO3

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Discuss current approaches and methodologies underpinning AI
  • LO2. Evaluate the potential roles and impact of AI in medical imaging
  • LO3. Critically evaluate previous validations, ethical and regulatory considerations of clinical AI.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been applied to the assessment and outline. In response to staff and student reviews of previous unit offerings, I have added examples of commercially available AI tools in each of the lectures.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.