Course Title: Machine Learning in Medical Diagnostics

Part A: Course Overview

Course Title: Machine Learning in Medical Diagnostics

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:  https://policies.rmit.edu.au/document/view.php?id=209

Please read the Student website for additional requirements of in-person attendance:  https://www.rmit.edu.au/covid/coming-to-campus 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 



Course Coordinator: Prof. Margaret Lech

Course Coordinator Phone: +61 3 9925 1028

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

Course Coordinator Availability: By appointment.


Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge

You will need to have some working knowledge of MATLAB, Python, or C/C++ programming before commencing this course.


Course Description

This course will introduce you to biomedical applications of image enhancement, image restoration, adaptive filtering of audio signals, and machine learning.

The theory presented in the pre-recorded lectures will be applied to solve practical problems in laboratory sessions. The laboratory assignments will introduce advanced computational methods for biomedical imaging and analysis based on MATLAB biomedical signal processing tools.

Topics to be investigated include:

  1. Introduction to biomedical image processing
  2. Colour images
  3. Image filtering, enhancement, and de-blurring
  4. Image restoration, segmentation, and edge detection
  5. Adaptive filtering and source separation
  6. Hearing aids
  7. Machine learning for supervised data classification
  8. Machine Learning for unsupervised data clustering
  9. Fundamentals of neural network modelling methods

If you are enrolled in this course as a component of your Bachelor Honours Program, your overall mark will contribute to the calculation of the weighted average mark (WAM).

See the WAM information web page for more information.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the following Program Learning Outcomes of the BH069 Bachelor of Engineering (Biomedical Engineering) (Honours):

  • PLO1: Demonstrate an in-depth understanding and knowledge of fundamental engineering and scientific theories, principles and concepts and apply advanced technical knowledge in specialist domain of engineering. 
  • PLO2: Utilise mathematics and engineering fundamentals, software, tools and techniques to design engineering systems for complex engineering challenges.    
  • PLO3: Apply engineering research principles, methods and contemporary technologies and practices to plan and execute projects taking into account ethical, environmental and global impacts.     
  • PLO4: Apply systematic problem solving, design methods and information and project management to propose and implement creative and sustainable solutions with intellectual independence and cultural sensitivity. 
  • PLO5: Communicate respectfully and effectively with diverse audiences, employing a range of communication methods, practising professional and ethical conduct.
  • PLO6: Develop and demonstrate the capacity for autonomy, agility and reflection of own learning, career and professional development and conduct.  
  • PLO7: Collaborate and contribute as an effective team member in diverse, multi-level, multi-disciplinary teams, with commitment to First Nations Peoples and globally inclusive perspectives and participation.   


Course Learning Outcomes

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

  1. Explain basic concepts of biomedical image processing.
  2. Explain principles of hearing aids design.
  3. Explain and apply principles of audio signal separation in the context of medical applications.
  4. Understand the principles of machine learning in relation to medical diagnostic practices.
  5. Apply image processing and machine learning techniques to basic biomedical applications.
  6. Communicate your designs and test findings through oral and written reports.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online, or a mix of both.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through Canvas and the RMIT Student website.

These resources include:

  1. A weekly step-by-step guide to your study on Canvas.
  2. PowerPoint slides and video recordings of lectures.
  3. Written and video recorded lectures and instructions for laboratory assignments.
  4. Access to MATLAB software and programming platform.
  5. Consultations and meetings with the Course Coordinator via video links.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help, and a range of study support services. For further information, please visit the Library page on the RMIT University website and the RMIT Student website.


Overview of Assessment

This course has no hurdle requirements.

You will be assessed on how well you meet the course learning outcomes and on your development against the program learning outcomes.

Assessment tasks are directly aligned with each Course Learning Outcome.  They are as follows:

Assessment 1: Laboratory assessment
Weighting: 25%
This assessment task supports CLOs 1-6.

Assessment 2: Individual timed assignment within 24 hours window
Weighting: 25%
This assessment task supports CLOs 3- 4.

Assessment 3: Team project report and presentation
Weighting: 30% (10% Presentation+ 20% Report)
This assessment task supports CLOs 1-6.

Assessment 4: Final timed assessment test within 24 hours window
Weighting 20%
This assessment task supports CLOs 5-6

 

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.