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Unit outline_

ELEC5622: Signals, Software and Health

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit is related to health informatics and focuses on introducing the acquisition, processing, and analysis of medical imaging signals. It introduces multiple widely used medical imaging techniques such as MRI, diffusion MRI, X-ray, and CT, as well as both the conventional and deep learning based image processing and machine learning methods to analyse medical image data for diagnosis. During the course, some commonly used software and platforms for medical image analysis, especially for brain image analysis, will also be covered.

Unit details and rules

Academic unit School of Electrical and Computer Engineering
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Mathematics (linear algebra and probabilities) and basic programming skills (python/matlab/C++/java)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Luping Zhou, luping.zhou@sydney.edu.au
Lecturer(s) Luping Zhou, luping.zhou@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final Exam
Close-book exam
40% Formal exam period 2 hours
Outcomes assessed: LO2 LO3
Assignment Lab report
Individual work
10% Week 05
Due date: 26 Aug 2024 at 23:59
two lab reports
Outcomes assessed: LO1 LO2 LO3 LO5
Tutorial quiz Quiz
Quiz in Canvas; Multiple Choice Questions
10% Week 08
Due date: 16 Sep 2024 at 15:00
N/A
Outcomes assessed: LO2 LO3
Assignment Project 1
Group work
20% Week 09
Due date: 29 Sep 2024 at 23:59
N/A
Outcomes assessed: LO3 LO4 LO5 LO6
Assignment Project 2
Group work
20% Week 13
Due date: 03 Nov 2024 at 23:59
N/A
Outcomes assessed: LO3 LO4 LO5 LO6

Assessment summary

  • Lab Report: Two laboratory reports on medical image processing
  • Project: Two projects using conventional and deep learning methods for brain and cell image classification
  • Quiz: Concepts of signals, software and health used in the project.
  • Final Exam: 2 hours 

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 Introduction (medical imaging signals, categories, applications, image planes, evaluation, etc.) Lecture (2 hr) LO2 LO3
Week 02 Lecture: Magnetic Resonance Imaging (MRI) – physical principles, spatial localization, and image formation; Tutorial 1: Brain image processing Lecture and tutorial (4 hr) LO1 LO2
Week 03 Lecture: Diffusion MRI – principles, scalar maps, and tractography; Lab 1: Brain image processing Lecture and tutorial (4 hr) LO1 LO2
Week 04 Lecture: X-ray and CT – principles, systems and image formation; Tutorial 2: Python basics Lecture and tutorial (4 hr) LO1 LO2 LO5
Week 05 Lecture: PET imaging –principles and image formation; Lab 2: Medical image analysis (edge extraction, k-means, svm using python) Lecture and tutorial (4 hr) LO1 LO2
Week 06 Lecture: medical image analysis – brief introduction about conventional methods for medical image classification and segmentation; Lab 3: Medical image analysis (continue) Lecture and tutorial (4 hr) LO3 LO4 LO5
Week 07 Lecture: medical image analysis – feature extraction and selection; Project 1: AD classification Lecture and tutorial (4 hr) LO3 LO4 LO5 LO6
Week 08 Lecture: Quiz; Project 1: AD classification (continue) Lecture and tutorial (4 hr) LO3 LO4
Week 09 Lecture: Neural net work basics; Tutorial 3: Python for deep learning Lecture and tutorial (4 hr) LO3 LO4
Week 10 Public Holiday Lecture and tutorial (4 hr) LO5 LO6
Week 11 Lecture: medical image classification with deep learning methods; Project 2: Cell image classification using deep learning Lecture and tutorial (4 hr) LO3 LO4 LO5 LO6
Week 12 Lecture 12: Medical image segmentation with deep learning methods; Project 2: Cell image classification using deep learning (continue) Lecture and tutorial (4 hr) LO3 LO4
Week 13 Lecture 13: Review; Project 2: Cell image classification using deep learning (continue) Lecture and tutorial (4 hr) LO5 LO6

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. use appropriate software platforms to process and analyse medical imaging signals
  • LO2. explain the principles of common medical imaging techniques and understand the foundations of how 3D medical images are formed from these signals.
  • LO3. understand and apply the common techniques for medical image processing and analysis, including both the conventional and the deep learning based methods.
  • LO4. use the existing medical image processing and machine learning toolboxes for medical image analysis.
  • LO5. write professional technical reports and make presentations to communicate complex materials in clear and concise terms.
  • LO6. develop basic team work and project management skills through a group project

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

Alignment with Competency standards

Outcomes Competency standards
LO1
Engineers Australia Curriculum Performance Indicators - EAPI
2.2. Application of enabling skills and knowledge to problem solution in these technical domains.
LO3
Engineers Australia Curriculum Performance Indicators - EAPI
1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
LO4
Engineers Australia Curriculum Performance Indicators - EAPI
1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
LO5
Engineers Australia Curriculum Performance Indicators - EAPI
2.2. Application of enabling skills and knowledge to problem solution in these technical domains.
3.1. An ability to communicate with the engineering team and the community at large.
3.4. An understanding of and commitment to ethical and professional responsibilities.
4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
LO6
Engineers Australia Curriculum Performance Indicators - EAPI
3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams.
5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
5.6. Skills in the design and conduct of experiments and measurements.
5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.

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

Changes that have been made to the unit since it was last offered: learning activities

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.