Course Title: Biomedical Signal and Image Processing

Part A: Course Overview

Course Title: Biomedical Signal and Image Processing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

OENG1105

City Campus

Undergraduate

125H Electrical & Computer Engineering

Face-to-Face

 Sem 2 2016

OENG1136

Bundoora Campus

Undergraduate

172H School of Engineering

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023,
Sem 1 2024

Course Coordinator: Prof. Margaret Lech

Course Coordinator Phone: +61 3 9925 1028

Course Coordinator Email: margaret.lech@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study:
You are expected to have completed Signals and Systems 1 or other equivalent studies.

Assumed Knowledge:
You should be able to write MATLAB applications to solve typical signal processing or electrical/electronic engineering problems.


Course Description

Biomedical Signal and Image Processing will introduce you to the theory and medical diagnostic applications of machine learning and image processing.

The theory presented in the pre-recorded lectures will be applied to solve practical problems in laboratory sessions and demonstrations. The group design project will introduce you to an advanced machine learning diagnosis. 

Topics to be investigated include machine learning fundamentals and selected medical diagnosis topics using machine learning. 

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes of the Bachelor of Engineering (Honours):

1.3 In-depth understanding of specialist bodies of knowledge within the engineering discipline.
2.1 Application of established engineering methods to complex engineering problem-solving.
2.2 Fluent application of engineering techniques, tools and resources.

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you will be able to:

  1. Understand the principles of machine learning.
  2. Understand how neural networks work.
  3. Explain the benefits of deep learning and transfer learning.
  4. Explain basic concepts and applications of biomedical image and signal processing.
  5. Understand the principles of medical diagnosis using machine learning.
  6. Understand the advantages and limitations of different diagnostic systems.
  7. Design and apply software-based diagnostic systems using machine learning.
  8. Provide a personal reflection on professional practice and contribution to the group project.
  9. Evaluate your designs and assess findings through data analysis, results, interpretation, and system performance metrics calculation.
  10. Communicate your project outcomes through written reports. 


Overview of Learning Activities

This course is delivered through the following Learning Activities:

  1. Listening to pre-recorded lectures, where course material is presented through slides and worked examples.
  2. Attending lectorials, where you will revisit the lecture material, practice solving problems and applying concepts. These lectorials will assist you in consolidating the course material and provide a means of feedback on your progress and understanding.
  3. Working on practice tests designed to test your knowledge of the most important concepts introduced in the lectures.
  4. Working on practice labs designed to guide you through the process of software development for practical applications of concepts introduced in the lectures.
  5. Completing written assignments testing an integrated understanding of the subject matter.
  6. Working on a practical group project that applies theoretical concepts to real-world scenarios, enhancing your ability to compile, analyse, and critically evaluate data.
  7. Conducting a private study that consists of working through the course material presented in pre-recorded lectures and practice labs.


Overview of Learning Resources

All learning resources for this course are available on Canvas, the University Learning Management System.

These resources include:

  • Weekly modules with step-by-step guidance on your weekly activities.
  • Weekly lecture notes and video recordings explaining the theory behind course topics.
  • Weekly practice tests providing feedback on your understanding of theory.
  • Practice labs with MATLAB software examples that allow practical implementation of the theory presented in lecture slides.
  • Laboratory instructions and recordings guiding through the group design projects.
  • MATLAB programming software that can be installed on your own computer.


Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Individual Home Assignment (not timed)- (total 20%)
It is an online quiz with short-answer questions.
This assessment task supports CLOs 1-3.

Assessment Task 2: Individual Lectorial Test (timed and timetabled)- (total 15%)
It is an on-paper, 30-minute test with a mixture of multiple-choice and short-answer questions.
This assessment task supports CLOs 4-6.
This assessment is a timed and timetabled assessment of less than 2 hours duration that students must attend on campus.

Assessment Task 3: Individual Final Assessment (timed and timetabled) -(total 25%)
It is an on-paper, 70-minute test with short-answer questions.
This assessment task supports CLOs 1-6.
This assessment is a timed and timetabled assessment of less than 2 hours duration that students must attend on campus.

Assessment Task 4: Group Design Project Assessment - (total 40%):
Part 1- Mid-Semester Progress Report (15%)
Online submission is required.
Part 2- Final Report (25%)
Online submission is required.
This assessment task supports CLOs 7-10.

Note: If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.