CET873 AI for Bioimage, Signal analysis and Healthcare (Basic)

Course Provider

School of Computer Science and Engineering (SCSE)



Academic Unit



Biomedical image and signal processing for healthcare is experiencing tremendous growth worldwide, and biomedical jobs is one of the fastest growing career fields worldwide. This field is fundamental to the understanding, visualizing, and quantifying of medical images and bio signals in clinical applications. With the help of artificial intelligence and machine learning techniques, disease diagnosis will be easier, faster, and accurate leading to significant development in medicine in general. The goal of this course is to help learners develop basic skills in computational biomedical image and signal processing using the Python programming language and help them to appreciate the considerations in developing practical solutions for this domain.

This course is part of:

- Graduate Certificate in Artificial Intelligence and AI Plus 
- FlexiMasters in Artificial Intelligence and AI Plus 

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At the end of the course, learners  are able to:

  • Acquire basic knowledge on the fundamentals of biomedical image, signal processing and healthcare analysis
  • Understand how to apply artificial intelligence and machine learning methods in a broad range of biomedical and clinical applications
  • Hands-on coding in python mentored by Scientists for a small project on biomedical image, signal processing or healthcare data using various artificial intelligence or machine learning methods.
  • Appreciation for considerations in deployment of practical AI and ML solutions for biomedical and clinical applications.


AI for Biomedical imaging

  • Overview of Biomedical imaging analysis
  • AI for medical imaging, I/O, visualization, manipulation, and structure segmentation
  • Deep learning for X-Ray and CT images
  • Case: MRI Segmentation

AI for Biomedical Time Series and Healthcare Data Analysis

  • Overview of Biomedical time series and healthcare data analysis
  • Digital Frequency and Spatial Filtering
  • Machine Learning and Classification
  • Data Pre-processing and feature engineering
  • Multivariate structured data processing
  • Case: Signal processing on EEG data
  • Case: Clinical Informatics: Predictive Analytics with Multimodal Data
  • Case: Decision Support with EHR Data

Advanced Topics and Translational Considerations

  • Semi-supervised learning
  • Anomaly detection & Deployment considerations for AI innovations in healthcare
  • Emerging research directions
  • Frameworks for practical translation

No pre-requisites are needed to enrol in the individual courses. 

Participants need to take note that those without qualifications or prior working experience in relevant engineering or the related fields, may find the course contents challenging.

Standard Course Fee: S$1,944

SSG Funding Support

Course fee payable after SSG funding, if eligible under various schemes

Fee BEFORE funding & GST

Fee AFTER funding & 8% GST

Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding)



Enhanced Training Support for SMEs (ETSS)


SCs aged ≥ 40 years old
SkillsFuture Mid-career Enhanced Subsidy (MCES)
(Up to 90% funding)

• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

    Read more about funding

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