Research Projects
Big Data-Based Spine Disease Progression Prediction

Our team has established a big dataset with spine MRIs and clinical labels, as well as developed automated novel pipelines for feature detection and segmentation. Our methods include conventional machine learning and deep learning, image processing, etc. These novel approaches can significantly reduce the manual workload of clinical practice as well as provide consistent results. We are looking for a suitable candidate to expand the dataset and established a systematic registry that contains comprehensive medical data, including image (MRI, CT, Xray), clinical information, and follow-up, for multi-domain big data analysis tasks in spine clinic. Technically, a data structure for the organization, storage, and translation of the dataset needs to be designed. Research involves in human machines interactions resulting in a clinical friendly UI interface module of the dataset to the upload and download the data flexibly for different tasks, with encryption, such as image enhancement, auto-diagnosis, image segmentation, pathology predication. Our centre is the one of the largest spine centres in Hong Kong and routinely screen over 200 patients with spine disorders per week. This presents a guaranteed opportunity for satisfied patient sample size for this project.

Professor JPY Cheung, Department of Orthopaedics and Traumatology

Graduated from the University of Hong Kong with a Bachelor of Medicine and Bachelor of Surgery (MBBS), Professor Jason Cheung trained as an Orthopaedic Surgeon at the Queen Mary Hospital. He is a Clinical Professor with the Department of Orthopaedics and Traumatology at the University of Hong Kong. His main research interests are paediatric growth and spinal deformity, developmental lumbar spinal stenosis, management of cervical myelopathy and orthopaedic infections.

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:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

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 rpgmed@hku.hk.

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.