Outline

Overview

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.

Details

Academic unit	Electrical and Information Engineering
Unit code	ELEC5622
Unit name	Signals, Software and Health
Session, year ?	Semester 2, 2022
Attendance mode	Normal day
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	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 and contact details

Coordinator	Luping Zhou, luping.zhou@sydney.edu.au
Lecturer(s)	Luping Zhou , luping.zhou@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final Exam Open-book exam	40%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Lab report Individual work	10%	Week 05 Due date: 04 Sep 2022 at 23:59	two lab reports
Outcomes assessed:
Tutorial quiz	Quiz Quiz in Canvas; Multiple Choice Questions	10%	Week 08	N/A
Outcomes assessed:
Assignment	Project 1 Group work	20%	Week 09	N/A
Outcomes assessed:
Assignment	Project 2 Group work	20%	Week 12	N/A
Outcomes assessed:
= group assignment ? = Type C final exam ?

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.

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction (medical imaging signals, categories, applications, image planes, evaluation, etc.)	Lecture (2 hr)
Week 02	Lecture: Magnetic Resonance Imaging (MRI) – physical principles, spatial localization, and image formation; Tutorial 1: Brain image processing	Lecture and tutorial (4 hr)
Week 03	Lecture: Diffusion MRI – principles, scalar maps, and tractography; Lab 1: Brain image processing	Lecture and tutorial (4 hr)
Week 04	Lecture: X-ray and CT – principles, systems and image formation; Tutorial 2: Python basics	Lecture and tutorial (4 hr)
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)
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)
Week 07	Lecture: medical image analysis – feature extraction and selection; Project 1: AD classification	Lecture and tutorial (4 hr)
Week 08	Lecture: Quiz; Project 1: AD classification (continue)	Lecture and tutorial (4 hr)
Week 09	Public Holiday	Lecture and tutorial (4 hr)
Week 10	Lecture: Neural net work basics; Tutorial 3: Python for deep learning	Lecture and tutorial (4 hr)
Week 11	Lecture: medical image classification with deep learning methods; Project 2: Cell image classification using deep learning	Lecture and tutorial (4 hr)
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)
Week 13	Lecture 13: Review; Project 2: Cell image classification using deep learning (continue)	Lecture and tutorial (4 hr)

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

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
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 2.2. Application of enabling skills and knowledge to problem solution in these technical domains.

	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
	Engineers Australia Curriculum Performance Indicators - 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;.
	Engineers Australia Curriculum Performance Indicators - 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.
	Engineers Australia Curriculum Performance Indicators - 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.

Closing the loop

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

Key dates
Key dates through the academic year, including teaching periods, census, payment deadlines and exams.
Student administration
Enrolment, course planning, fees, graduation, support services, student IT
Expectations of student conduct
Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity
Academic appeals
Academic appeals process, special consideration, rules and guidelines, advice and support
Learning and teaching policy
Policy register, policy search
Financial support
Scholarships, interest free loans, bursaries, money management
Study resources
Learning Centre, faculty and school programs, Library, online resources
Health and support
Student Centre, counselling & psychological services, University Health Service, general health and wellbeing

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.

Current students

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Closing the loop

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ELEC5622: Signals, Software and Health

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Closing the loop

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect