Programme(s) to which this project applies:
|☑ MPhil/PhD||☑ MRes[Med]||☑ URIS|
Our lab focuses on utilisation of quantitative data that can be found in medical images such as CT, MRI and PET scans and leverage data science and machine learning approach to derive novel insights into disease processes. There are a few broad areas that we focus on as follows.
Dr VV Vardhanabhuti, Department of Diagnostic Radiology
Dr Vardhanabhuti completed his medical degree at Guy's, King's and St Thomas' School of Medicine in London, UK in 2005. He had subsequent training in London, Oxford, Plymouth, Exeter, and completed his Radiology training at Imperial College London, UK whilst also completing a PhD during his residency.
He has active interests in data science, machine learning, and artificial intelligence while engaging with various research projects relating to medical imaging, with the goal of early clinical translation to benefits patients. He has wide-ranging projects in the AI space including the use of quantitative radiomics in cancer prognostication, using deep learning model as a tool for automatic segmentation and cancer detection, big data using electronic patient records, etc. More recently he has been engaged in several projects applying machine learning and artificial intelligence techniques in tackling the COVID-19 pandemic.
For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description. Interested candidates are advised to enclose with your email:
Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to email@example.com.
HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.
HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.