Note: This unit version is currently under review and is subject to change!

ELEC5622: Signals, Software and Health (2020 - Semester 2)

Download UoS Outline

Unit: ELEC5622: Signals, Software and Health (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Electrical & Information Engineering
Unit Coordinator/s: Dr Zhou, Luping
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: 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.
Assumed Knowledge: None.
Timetable: ELEC5622 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Project Work - in class 2.00 1 5
3 Laboratory 2.00 1 6
4 Independent Study 6.00 13
T&L Activities: Students are expected to undertake the prescribed reading and work on homework exercises and assignments.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Unassigned Outcomes
1. Be able to use appropriate software platforms to process and analyse medical imaging signals
2. Be able to explain the principles of common medical imaging techniques and understand the foundations of how 3D medical images are formed from these signals.
3. Be able to understand and apply the common techniques for medical image processing and analysis, including both the conventional and the deep learning based methods.
4. Be able to use the existing medical image processing and machine learning toolboxes for medical image analysis.
5. Be able to write professional technical reports and make presentations to communicate complex materials in clear and concise terms.
6. Be able to develop basic team work and project management skills through a group project.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Lab reports (two) No 10.00 Week 6 (Friday) 1, 2, 4, 5,
2 Quiz 1 No 10.00 Week 8 2,
3 Project 1 Yes 15.00 Week 10 1, 3, 5, 6,
4 Project 2 Yes 15.00 Week 13 3, 4, 5, 6,
5 Final exam No 50.00 Exam Period 2, 3,
Assessment Description: * indicates an assessment must be repeated if a student misses it due to special consideration.

There may be statistically and educationally defensible methods used when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with grade descriptors.

15% penalty will be applied for each day. Submissions that are late for one week will be given ZERO marks.

The University has authorised and mandated the use of text-based similarity detecting software Turnitin for all text-based written assignments.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Grading Schema High Distinction, HD (85-100). Show through independent work, ability to use and integrate several theoretical frameworks and tools to build health systems.

Distinction, DI (75-84). Compare data analysis and visualization through an analysis of advantages and disadvantages. Be able to relate pros and cons of using a technology in a health problem. Show consistent high quality analysis, design and communication skills.

Credit, CR (65-74). Be able to show consistent analytical and communication skills. Deliver a working prototype with quality documentation. Proficiency iusing data analysis and visualization tools (e.g. Matlab packages).

Pass, PA (50-64). Being able to complete a signal, software and health project. Showing motivation and initiative. Inconsistent programming and communication skills
Policies & Procedures: See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Lecture: Introduction (medical imaging signals, categories, applications, image planes, evaluation, etc.)
Week 2 Lecture: Magnetic Resonance Imaging (MRI) – physical principles, spatial localization, and image formation
Week 3 Lecture: Diffusion MRI – principles, scalar maps, and tractography
Week 4 Lecture: X-ray and CT – principles, systems and image formation
Week 5 Lecture: PET imaging –principles and image formation
Week 6 Lecture: Medical image analysis – brief introduction about conventional methods for medical image classification and segmentation
Assessment Due: Lab reports (two)
Week 7 Lecture: Medical image analysis – feature extraction and selection
Week 8 Quiz
Assessment Due: Quiz 1
Week 9 Lecture: Neural net work basics
Week 10 Lecture: Medical image classification with deep learning methods
Assessment Due: Project 1
Week 11 Lecture: Medical image segmentation with deep learning methods
Week 12 Lecture: Medical image synthesis with deep learning methods
Week 13 Review
Assessment Due: Project 2
Exam Period Assessment Due: Final exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Electrical (till 2014) 2014
Electrical (Computer) (till 2014) 2014
Electrical (Power) (till 2014) 2014
Electrical (Telecommunications) (till 2014) 2014
Electrical Mid-Year 2016, 2017, 2018, 2019, 2020, 2021
Electrical/ Project Management 2019, 2020
Electrical 2015, 2016, 2017, 2018, 2019, 2020, 2021
Electrical / Arts 2016, 2017, 2018, 2019, 2020
Electrical / Commerce 2016, 2017, 2018, 2019, 2020
Electrical / Medical Science 2016, 2017
Electrical / Music Studies 2016, 2017
Electrical / Project Management 2016, 2017, 2018, 2020
Electrical / Science 2016, 2017, 2018, 2019, 2020
Electrical/Science (Health) 2018, 2019, 2020
Electrical (Computer) 2015
Electrical / Law 2016, 2017, 2018, 2019, 2020
Electrical (Power) 2015
Electrical (Telecommunications) 2015
Software Mid-Year 2016, 2017, 2018, 2019, 2020, 2021
Software/ Project Management 2019, 2020
Software 2015, 2016, 2017, 2018, 2019, 2020, 2021
Software / Arts 2016, 2017, 2018, 2019, 2020
Software / Commerce 2016, 2017, 2018, 2019, 2020
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018, 2019, 2020
Software/Science (Health) 2018, 2019, 2020
Software / Law 2016, 2017, 2018, 2019, 2020
Software Engineering (till 2014) 2014
Electrical/Science (Medical Science Stream) 2018, 2019, 2020
Graduate Certificate in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Certificate in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Health Technology Innovation 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Master of Engineering 2014, 2015, 2016, 2017, 2018, 2019, 2020
Master of Health Technology Innovation 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Master of IT/Master of IT Management 2015, 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Electrical) 2019, 2020
Master of Professional Engineering (Accelerated) (Intelligent Information Engineering) 2020
Master of Professional Engineering (Accelerated) (Software) 2019, 2020
Master of Professional Engineering (Electrical) 2014, 2015, 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Intelligent Information Engineering) 2020
Master of Professional Engineering (Software) 2014, 2015, 2016, 2017, 2018, 2019, 2020
Software/Science (Medical Science Stream) 2018, 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 3) No 0%
(7) Project and Team Skills (Level 3) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(3) Problem Solving and Inventiveness (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 4) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.