Research Projects
Comparative Evaluation of Radiomics and Deep Learning Features for Predictive Modeling in Gynecological Cancers

This project aims to evaluate and compare radiomics and deep learning (DL) feature extraction techniques to advance diagnostic accuracy and clinical decision-making in gynecological cancers. By integrating both methodologies, we seek to uncover effective strategies for predicting treatment outcomes and identifying imaging biomarkers that could aid in early diagnosis, treatment planning, and prognosis. Students will gain valuable opportunities to pursue research paths that match their interests and the primary supervisor’s expertise.

Key Objectives (not limited to):

1. Advancing Tumor Imaging and Predictive Analytics: Develop insights on how radiomic and DL features characterize tumor morphology, texture, and function in gynecologic malignancies.
2. Assessing Predictive Power for Clinical Relevance: Determine whether radiomics, DL features, or a combination yields the highest accuracy in predicting clinical outcomes and response to treatment.
3. Identifying Biomarkers for Patient Care: Build robust models to detect imaging biomarkers that correlate with disease progression, treatment efficacy, and recurrence risk.

Professor EYP Lee, Department of Diagnostic Radiology

Professor Elaine Lee received her undergraduate medical training at the University of Nottingham, UK. She completed her specialist training in radiology and fellowship in Wales, UK. She joined the Department of Diagnostic Radiology at the University of Hong Kong in 2010. Here, she developed her subspecialty interest in gynae-oncology, specifically in the application of functional and molecular imaging techniques in this field. She is the first advocate of intravoxel incoherent motion imaging (IVIM), a novel MRI technique in cervical cancer. More recently, her work has evolved around the applications of radiomics and AI in gynaecological cancer imaging. Her published works include > 100 original articles and abstracts in the field of Radiology and Nuclear Medicine and an invited book chapter contribution in the seminal book on IVIM by Professor Le Bihan, a pioneer and world-renowned researcher on diffusion MRI technology. She is the primary investigator and recipient of competitive national grants in advancing the research in gynae-oncology imaging with established international and national collaborations. She is a Fellow of the International Cancer Imaging Society since 2022. 

Apart from Research and Scholarship, Professor Lee has taken an active role in Teaching and Learning.  She has led the first structured undergraduate ultrasound curriculum in Hong Kong, leading to the award of HKU Faculty of Medicine Teaching Medal in 2019. She has been instrumental in introducing new pedagogies in the undergraduate teaching with several pedagogy publications.

Biography
HKU Scholars Hub
ORCID
eyplee77@hku.hk

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.