Current project

VIP Snapshot

Medical imaging is vital when diagnosing or tracking the progress of an illness. Join the team using advanced computational methods to improve patient outcomes.

VIP ChallENG research goals

Together with medical and computing students, you’ll conduct research and develop techniques and tools for automated biomedical image analysis, visual analytics and biomedical informatics.

The research includes:

  • Medical image and clinical dataset acquisition and storage 
  • Medical image annotation, including anatomy and disease states, human computer interaction and user interaction design issues 
  • Understanding and devising new computer vision, machine and deep learning techniques for medical image computing and analysis 
  • Exploring new multimodal machine learning models for medical image and clinical datasets 
  • Statistical and machine learning methods for outcomes prediction and survival analysis 
  • Interpreting machine and deep learning models for the physician 
  • User interaction design for the physician 

Research Areas:

  • Anatomy 
  • Anatomical and functional imaging 
  • Artificial intelligence 
  • Machine and deep learning 
  • Computer vision 
  • Biomedical image computing / biomedical image analysis 
  • Data analytics / visual analytics 
  • User interaction design 
  • Biomedical informatics
  • Medicine 
  • Medical Sciences 
  • Computer Science 
  • Computer Engineering 
  • Software Engineering 
  • Information Technology 
  • BioInformatics  
  • Machine Learning/Algorithm Design 
  • Artificial Intelligence 
  • Biomedical Engineering 
  • Mechatronics Engineering 
  • Electrical Engineering 
  • Design 
Medical Imaging

Explore the InsightMed Research Areas

Investigating the potential of advanced computational methods for medical imaging, below are the various aspects you can choose to explore.

Understanding anatomy and function in the context of a variety of medical imaging modalities such as X-rays, ultrasound, CT, MRI and PET.

  • Familiarisation with medical imaging software: viewer, visualisation, and manipulation in 2D and 3D 
  • Familiarisation with medical imaging annotation software, customisation for applications in 2D and 3D 
  • Annotation of anatomy in multimodal medical image datasets for multiple modalities (X-ray, CT, MRI and PET) and organs (lungs, prostate, vertebrae, kidneys and liver) 
  • Annotation of lesions and changes relevant to specific diseases, including cancer and chronic conditions 
  • Identification and localisation of organs of interest using computer vision, machine and deep learning 
  • Organ segmentation based on annotations in normal and abnormal (diseased) cases  
  • Semantic segmentation of disease conditions 
  • Spatial and temporal registration of organs in multiple modalities at multiple time points 
  • Quantification (measurements) of normal and abnormal organs, and disease conditions 
  • Early screening and faster, more accurate diagnosis of chronic disease and cancer 
  • Facilitating the discovery of imaging biomarkers 
  • Advancing the understanding of brain function in health and disorders 
  • Support for radiotherapy planning and distributed learning in medical settings 
  • High-throughput image analysis for drug screening 
  • Multimodal learning from images and clinical datasets, including predictors of outcomes, recurrence and survival 
  • Discovery of imaging and non-imaging biomarkers from images and clinical datasets

Team Academic Leads