Research Project

Photo by Toa Heftiba on Unsplash

Survival prediction, one very important task in prognosis, now meets many deep learning approaches. Several deep survival learning models are proposed for predicting the surivals of cancer patients from the provided multi-modal data of patients.

Technological advances create a great opportunity to provide multi-view data for patients including 3D/4D radiological imaging, High resolutional pathological slides, gene expression, etc. For example in imaging data, traditional surival models mainly rely on explicitly-designed handcrafted features. This process highly relies on domain expert and human intervention which could introduce human bias and limit the use on large scale data.

Deep learning have been successfully used in many medical imaging problem including detection, classification, segmentation. Survival prediction describes how patients will survival in the future. Depend on the event, it also includes the predication of Overall Survival (OS), Disease Free Survival (DFS) or other prognostic factor prediction. One of my research work is survival prediction using various imaging modality including CT, pathology and genomics. The following show papers that I authored or co-authored in recent years.

CT

Pathology

Image-Omics

Senior Algorithm Expert

My research interests include medical image analysis and precision medicine.