Outline

Overview

Biomedical imaging technology is a fundamental element of both clinical practice and biomedical research, enabling the visualisation of biological characteristics and function often in a non-invasive fashion. The advancement of digital scanning technologies alongside the development of computational tools has driven significant progress in medical image analysis tools that support clinical decisions and the analysis of data from biological experiments. The focus of this unit will be the development of fundamental computational skills and knowledge in biomedical imaging, including data acquisition, formats, visualisation, segmentation, feature extraction, and machine learning based image analysis. On completion of this unit, students will be able to engineer and develop solutions for different biomedical imaging tasks encountered across a variety of use cases: clinical practice (e.g., computerised disease detection and diagnosis), research (e.g., cell video analysis), and industry (e.g., fabrication of customised implants from patient image data).

Unit details and rules

Academic unit	Biomedical Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	An understanding of biology (1000-level), experience with programming (ENGG1801, ENGG1810, INFO1110, BMET2922 or BMET9922)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Sandhya Clement, sandhya.clement@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Laboratory report Students will be given code snippets related exercise to work through.	15%	Multiple weeks	Code to solve the laboratory exercise	AI allowed
Outcomes assessed:
Written work	Image Processing Part A-Report Students will be given a set of image analysis tasks to work through.	10%	Week 06 Due date: 03 Apr 2026 at 23:59	Analysis report and code	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz 1 paper based Quiz covering Lecture and Lab content from Week 1- Week 6	15%	Week 07	90 min	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Image processing Part B-Demonstration Demonstration of image processing assignment, that includes coding and analysis.	10%	Week 07	5-10 min demonstration & Q&A	AI prohibited
Outcomes assessed:
Written work	Image Classification Part A-Report A research paper describing a solution to a biomedical image analysis task.	20%	Week 12 Due date: 22 May 2026 at 23:59	6-8 pages and code in IEEE paper format.	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz 2 Paper based Quiz covering the lecture and lab content from Week 8-Week 12	15%	Week 13	90 min	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Image Classification Part B-Demonstration Demonstration of classification assignment that includes coding and detailed analysis.	15%	Week 13	10-15 min demonstration including Q&A	AI prohibited
Outcomes assessed:
= hurdle task ?

Assessment summary

Image Processing Assignment: This is an individual assignment. Students will be given a dataset and will be asked to conduct a set of specific analyses on the biomedical image data. The assignment will ask students to to implement some code, conduct the analysis, write a brief report (Part A) (10% weightage), and then demonstrate their analysis (Part B) (10%) to the teaching team. The report and any code must be submitted on Canvas in Week 6. During the next laboratory class (Week 7), students will demonstrate and explain their findings. Part B is a hurdle task. Students who do not complete the demonstration, or who cannot show sufficient understanding of their own work, will not receive marks for the entire assessment.
Quiz 1 and 2: This quiz 1&2 will cover all material covered up until the end of Week 6 and 12 respectively, including both lecture and laboratory materials. Students can expect a mix of questions, which may include multiple choice, matching, and short answers. Questions will not be simply recall of facts but instead may ask students to apply their understanding. The quiz will be closed book. The students are allowed to bring an 1-sided A4 cheat sheet consolidating their study material for the Quiz.
Image Classification Assignment: Students will be given a biomedical imaging dataset and will be asked to implement image classification algorithms using the skills they have developed during laboratories. They will be expected to evaluate and test their algorithm, and present their findings in the form of a research conference paper. The paper and any code (Part A) must be submitted on Canvas (20% weigtage) in week 12. During the next laboratory class (Week 13), students (individually) will demonstrate and explain their findings (Part B) (15% weightage). It is expected students will work in pairs, with groups of 3 allowed for odd numbers of students. Part B is a hurdle task. Students who do not complete the demonstration, or who cannot show sufficient understanding of their own work, will not receive marks for the entire assessment.Also, teams may asked to complete self and peer review reflection associated with this assignment to moderate the report marks of individual's within the team if needed.
Laboratory Report: For each lab session starting from week 3, students are required to submit a short lab report (as pdf file) that includes all the code covered in the exercises with detailed comments, and if the problem requires it, please also provide your thoughts and solutions in short sentences. The submissions are expected only for the laboratory works done on Week 3, 4, 9 (8&9 combined), 10, 11 and 12. So, there will be a total of six lab reports, of which the five highest scoring reports will be counted toward your final grade. This is a hurdle task, students has to get minimum of 50% mark in lab report assessment to pass this unit.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (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	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 - 49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard. In such cases the student will receive the mark awarded by the faculty up to a maximum of 49.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Ongoing	Review of content, work on assessments, research readings, and online supplementary activities.	Self-directed learning (4 hr)
Week 01	Unit introduction	Lecture (2 hr)
Week 02	Biomedical image acquisition	Lecture (2 hr)
Week 02	Introduction to imaging toolboxes	Practical (3 hr)
Week 03	Fundamental image processing and analysis	Lecture (2 hr)
Week 03	Fundamental image processing and analysis	Practical (3 hr)
Week 04	Biomedical image segmentation	Lecture (2 hr)
Week 04	Biomedical image segmentation	Practical (3 hr)
Week 05	Biomedical image visualisation	Lecture (2 hr)
Week 05	Biomedical image visualisation	Practical (3 hr)
Week 06	Biomedical image registration and fusion	Lecture (2 hr)
Week 06	Biomedical image registration and fusion.	Practical (3 hr)
Week 07	Quiz 1 (Lecture and Lab content from Week 1-Week 6)-on campus, during lecture time	Lecture (2 hr)
Week 07	Assignment 1 Demonstration	Practical (3 hr)
Week 08	Artificial intelligence in biomedical imaging	Lecture (2 hr)
Week 08	Image classification and prediction	Practical (3 hr)
Week 09	Deep learning and convolutional neural networks in biomedical imaging - Part 1	Lecture (2 hr)
Week 09	Image classification and prediction	Practical (3 hr)
Week 10	Deep learning and convolutional neural networks in biomedical imaging - Part 2	Lecture (2 hr)
Week 10	Convolutional neural networks	Practical (3 hr)
Week 11	Neuroimage analysis Part 1-Fundamentals	Lecture (2 hr)
Week 11	Convolutional neural networks	Practical (3 hr)
Week 12	Neuro image Analysis Part 2-Applications	Lecture (2 hr)
Week 12	Convolutional neural networks	Practical (3 hr)
Week 13	Quiz 2 (Lecture and Lab content from Week 8-Week 12)-on campus, during lecture time	Lecture (2 hr)
Week 13	Assignment 2 Demonstration	Practical (3 hr)

Attendance and class requirements

Students are expected to attend all classes.

The University attendance policy can be found here (see clause 68): https://www.sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2014/378&RendNum=0

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.

Required readings

References and readings will be provided on Canvas.

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.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Understand the context, sources, and applications of biomedical imaging and image analysis.
LO2. Understand and apply a variety of fundamental image processing techniques across a variety of biomedical imaging contexts.
LO3. Appraise the effectiveness of different biomedical image analysis algorithms and tools using standard performance metrics.
LO4. Create solutions for prediction and classification tasks in biomedical imaging through the combination of image processing and machine learning techniques.
LO5. Implement prototype software solutions for biomedical image analysis tasks using existing software packages and libraries.
LO6. Assess the strengths and limitations of emerging biomedical image analysis algorithms from research literature.

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

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Updated assignment structure, weightage and their category (open or closed). Also, most of the assessments have an associated hurdle component to ensure they are achieving the intended learning outcomes

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

BMET5933: Biomedical Image Analysis

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links