Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - CMPT 340 BLS1

Biomedical Computing (3)

Class Number: 5448

Delivery Method: Blended

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Wed, 4:30–5:20 p.m.
    Burnaby

    Jan 5 – Apr 10, 2026: TBA, TBA
    Burnaby

  • Prerequisites:

    Completion of 60 units including one of CMPT 125, 126, 128, 135, with a minimum grade of C- or CMPT 102 with a grade of B or higher.

Description

CALENDAR DESCRIPTION:

The principles involved in using computers for data acquisition, real-time processing, pattern recognition and experimental control in biology and medicine will be developed. The use of large data bases and simulation will be explored.

COURSE DETAILS:

Overview

Prerequisite note: We highly recommend that students have completed MATH 152 and MATH 232 as some course topics require knowledge of integration and working with vectors and matrices.

Biomedical computing is a rapidly emerging area that is revolutionizing medicine and biology. It focuses on the use of computational and mathematical techniques to accelerate scientific discovery and to improve diagnosis, treatment, and understanding of diseases. The general objectives of the course are: To give the student breadth in the topics related to the use of computers in biology, medicine, and healthcare, in both theory and application. To enable the student to converse knowledgeably with radiologists, physicians, and MDs about these topics and related technologies. To learn the technical basis of many aspects of the use of technology in biomedicine (e.g. data acquisition, biosignal/image processing and visualization, feature representation and extraction, data interpretation and machine learning), current and future trends, and their benefits and limitations.

Topics

  • Introduction and motivation to biomedical computing
  • Biodata (signals, images, point-clouds) modalities and representations
  • Fourier transform and convolution
  • Biosignal digitization (sampling theorem and quantization)
  • Biosignal enhancement  (temporal/spatial and frequency domain filtering)
  • Feature extraction from biodata (1D, 2D, 3D, shapes, networks)
  • Decision support (basic classification and multiple features/multiple classes)
  • Dimensionality reduction using principal component analysis and linear discriminant analysis (PCA and LDA)
  • Bayes' rule.
  • Machine learning techniques (decision forests, artificial/convolutional neural networks), time permitting
  • Project work: application of computational techniques to biomedical applications

 

COURSE-LEVEL EDUCATIONAL GOALS:

Learning Incomes

Upon entry, every student is expected to be able to:

  • write code using some programming or scientific computing language (e.g. C/C++, Java, Python, MATLAB)
  • basic familiarity with version control and markup languages (so you are ready to use or learn to use GitHub and LaTex on Overleaf)
  • understand documentation for using and calling functions from external libraries 
  • be able to follow instructions for installing software and performing other basic IT tasks
  • understand high-school level concept in physics, biology, and chemistry
  • understand high-school and first-year undergraduate level concepts in mathematics (e.g. calculus, probability, trigonometry, vectors and matrices)

Learning Outcomes

At the end of the course, students are expected to be able to:

  • define different biomedical data acquisition devices and biosignals
  • explain the different mathematical and computer representation of biodata modalities
  • give several examples of biomedical applications of computing
  • extract various types of features from multi-modal bio-data
  • appreciate the meaning and utility of some mathematical and computational methods and concepts and their application to biomedical data (e.g. Fourier analysis, convolution, correlation, n-D histogram, high-dimensional data, probability distributions, entropy, covariance matrix, eigen-decomposition)
  • understand the meaning of and perform basic feature selection and dimensionality reduction
  • appreciate how computing plays a role in decision support
  • represent some biomedical tasks (e.g. diagnosis) as classification problems
  • calculate basic classification evaluation measures (e.g. true positive, confusion matrix, area under ROC)
  • state and utilize Bayes’ theorem for estimating answers to health-related problems
  • appreciate the strengths and limitations of using computers for biomedical applications

Grading

NOTES:

Grading to be announced during the first week of classes.

Materials

RECOMMENDED READING:

Optional references and resources

  • Clinical Decision Support Systems: Theory and Practice, E. S. Berner, Springer, 2010, 9781441922236, 2nd Edition (3rd Edition 2016 also available)

  • Information Technology for the Health Professions, L. Burke and B. Weill, Pearson Prentice Hall, 9780132897648, 4th Edition (5th Edition 2021 also available)

  • PACS and Imaging Informatics: Basic Principles and Applications, H. K. Huang, Wiley, 2004, 9780471251231 (2nd Edition 2010 also available)

  • Neural Networks and Artificial Intelligence for Biomedical Engineering, D. L. Hudson, M. E. Cohen, Wiley, 1999, 9780780334045

  • Handbook of Medical Informatics 1st Edition, J. Bemmel, M. Musen, Springer Verlag, 1997, 9783540633518, Copies available via amazon. There is a hard copy version available to be signed out on the SFU library

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.