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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

ELEC5622: Signals, Software and Health

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, 2021
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

No

Teaching staff and contact details

Coordinator Luping Zhou, luping.zhou@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final Exam
40% Formal exam period 2 hours
Outcomes assessed: LO3 LO2
Assignment Lab report
10% Week 05 two lab reports
Outcomes assessed: LO1 LO2 LO3 LO5
Tutorial quiz Quiz
10% Week 08 N/A
Outcomes assessed: LO2 LO3
Assignment group assignment Project 1
20% Week 09 N/A
Outcomes assessed: LO3 LO4 LO5 LO6
Assignment group assignment Project 2
20% Week 12 N/A
Outcomes assessed: LO3 LO4 LO5 LO6
group assignment = group assignment ?
Type C final exam = 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 honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction (medical imaging signals, categories, applications, image planes, evaluation, etc.) (2 hr) LO2 LO3
Week 02 Lecture: Magnetic Resonance Imaging (MRI) – physical principles, spatial localization, and image formation; Tutorial 1: Brain image processing (4 hr) LO1 LO2
Week 03 Lecture: Diffusion MRI – principles, scalar maps, and tractography; Lab 1: Brain image processing (4 hr) LO1 LO2
Week 04 Lecture: X-ray and CT – principles, systems and image formation; Tutorial 2: Python basics (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) (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) (4 hr) LO3 LO4 LO5
Week 07 Lecture: medical image analysis – feature extraction and selection; Project 1: AD classification (4 hr) LO3 LO4 LO5 LO6
Week 08 Public Holiday (4 hr) LO5 LO6
Week 09 Lecture: Quiz; Project 1: AD classification (continue) (4 hr) LO3 LO4
Week 10 Lecture: Neural net work basics; Tutorial 3: Python for deep learning (4 hr) LO3 LO4
Week 11 Lecture: medical image classification with deep learning methods; Project 2: Cell image classification using deep learning (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) (4 hr) LO3 LO4
Week 13 Lecture 13: Review; Project 2: Cell image classification using deep learning (continue) (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.
LO2
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.
Changes that have been made to the unit since it was last offered: learning activities and assessment

Disclaimer

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