CET874 AI for Bioimage, Signal analysis and Healthcare (Intermediate)

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. Improved performance and increased adoptability of AI in healthcare has brought rich dividends in various applications of healthcare. We aim to explore these new applications in terms of Medical Image reconstruction, Decision support systems, Federated learning to enrich the course participants and update them with the recent developments. 

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 intermediate to advanced knowledge on biomedical image, signal processing and healthcare analysis
  • Case studies will illustrate the application of AI/ML/DL methods to a wide range of clinical applications.
  • Hands-on experience helps in development of project on biomedical image, signal processing or healthcare data using various artificial intelligence or machine learning methods. Participants will be guided by the experts in the field.
  • Deployment of practical AI and ML solutions for biomedical and clinical applications   - its limitations and advantages.  


Medical imaging modalities & AI applications

  • Overview of Biomedical imaging modulates
  • AI based image reconstruction applications
  • Synthetic medical image generation & applications

Advanced AI methods for Healthcare Data Analysis – Time series/Image analysis

  • Overview of Biomedical time series and healthcare data analysis
  • Advance AI based time series analysis methods - Case Studies based
  • Recent advances in AI/Machine Learning and Deep learning
  • Feature engineering methods and its significance
  • Multivariate structured data processing
  • Case studies: Signal processing / Clinical Informatics / Decision Support

Advanced Translational applications

  • Data Harmonization in medical data analysis
  • Federated learning for medical data analysis
  • Super resolution-based reconstruction
  • Deployment considerations for AI innovations in healthcare
  • Frameworks for practical translation – Case studies based

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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