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, BMET2922 or BMET9922).
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Ashnil Kumar, ashnil.kumar@sydney.edu.au
Lecturer(s)	Sandhya Clement, sandhya.clement@sydney.edu.au
Lecturer(s)	Ashnil Kumar, ashnil.kumar@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Exam Final Exam	45%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Image Processing Students will be given a set of image analysis tasks to work through.	20%	Week 06	Problem set and code.
Outcomes assessed:
Small test	Quiz Online quiz	15%	Week 07	Between 45 min-1 hour (see Canvas).
Outcomes assessed:
Assignment	Image Classification A research paper describing a solution to a biomedical image analysis task.	20%	Week 12	6-8 pages and code.
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Image Processing 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, and then demonstrate their analysis to the class. The report and any code must be submitted on Canvas. During the next laboratory class, students will demonstrate and explain their findings.
Quiz: This quiz will cover all material covered up until the end of Week 6, 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 open book.
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 must be submitted on Canvas. During the next laboratory class, students will demonstrate and explain their findings. It is expected students will work in pairs, with groups of 3 allowed for odd numbers of students.

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	Work of an exceptional standard
Distinction	75 - 84	Work of a very high standard
Credit	65 - 74	Work of a good standard
Pass	50 - 64	Work at an acceptable standard
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see 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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

A late penalty of 5% per day late (or part thereof) will be applied. After 10 days, a mark of zero will be given unless special consideration has been received.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Ongoing	Review of content, work on assessments, research readings, and online supplementary activities.	Independent study (4 hr)
Week 01	Unit introduction	Online class (2 hr)
Week 02	Biomedical image acquisition	Online class (2 hr)
Week 02	Introduction to imaging toolboxes	Computer laboratory (2 hr)
Week 03	Fundamental processing and analysis	Online class (2 hr)
Week 03	Fundamental image processing and analysis	Computer laboratory (2 hr)
Week 04	Biomedical image segmentation	Online class (2 hr)
Week 04	Biomedical image segmentation	Computer laboratory (2 hr)
Week 05	Biomedical image visualisation	Online class (2 hr)
Week 05	Biomedical image visualisation	Computer laboratory (2 hr)
Week 06	Biomedical image registration and fusion	Online class (2 hr)
Week 06	Biomedical image registration and fusion	Computer laboratory (2 hr)
Week 07	Quiz	Online class (2 hr)
Week 07	Assignment 1 Demonstration	Computer laboratory (2 hr)
Week 08	Artificial intelligence in biomedical imaging	Online class (2 hr)
Week 08	Image classification and prediction	Computer laboratory (2 hr)
Week 09	Deep learning and convolutional neural networks in biomedical imaging - Part 1	Online class (2 hr)
Week 09	Image classification and prediction	Computer laboratory (2 hr)
Week 10	Deep learning and convolutional neural networks in biomedical imaging - Part 2	Online class (2 hr)
Week 10	Convolutional neural networks	Computer laboratory (2 hr)
Week 11	Putting it all together - Case Study 1	Online class (2 hr)
Week 11	Convolutional neural networks	Computer laboratory (2 hr)
Week 12	Putting it all together - Case Study 2	Online class (2 hr)
Week 12	Convolutional neural networks	Computer laboratory (2 hr)
Week 13	Unit Review	Online class (2 hr)
Week 13	Assignment 2 Demonstration	Computer laboratory (2 hr)

Attendance and class requirements

Students are expected to attend all classes, as per the Faculty Resolutions: https://www.sydney.edu.au/handbooks/engineering/rules/faculty_resolutions.shtml

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.

This is the first time this unit has been offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

BMET5933: Biomedical Image Analysis

Semester 1, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect