Multimodal Learning for Cancer Diagnosis and Prognosis
Project Description
In clinical practice, heterogenous multi-modality data (e.g., radiology images, clinical history, reports, etc.) will be collected for cancer diagnosis and prognosis. With the development of advanced computational methods, various biomarker features can be extracted and mined for cancer analysis. This project aims to develop state-of-the-art deep learning methods for cancer diagnosis, treatment outcome prediction and prognosis by mining multi-modality data.
Students are encouraged to publish high-impact research papers.
Supervisor
CHEN, Hao
Quota
5
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
Develop advanced algorithms and draft papers.
Applicant's Learning Objectives
Grasp deep learning techniques; Publish high-quality research papers if possible.
Complexity of the project
Moderate