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.