Deep learning based Lung Nodule Detection and Cancer risk stratification through the effective Integration of Imaging and Electronic Medical Records

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Lung cancer is the leading cause of cancer related deaths in the UK with very low five- and ten-year survival rates. This is attributed to the fact that the cancer is typically diagnosed at an advanced stage as early detection is particularly challenging. Consequently, there is a clear need for automated and robust systems that facilitate its early detection, diagnosis and treatment. The goal of this project is to develop a system to improve patient management in the context of both lung cancer screening and incidental nodule discovery. The project objectives are defined to this end: Develop a fully supervised lung nodule detection framework for automatic assessment of low dose chest CTs. Develop a cancer risk stratification system based on the official clinical guidelines. Investigate the use of NLP tools for extracting useful information from electronic medical records (EMRs), for subsequent integration with the image analytics. Develop a weakly supervised nodule detection algorithm for the effective integration of NLP-derived data from radiology reports with imaging data (low-dose chest CTs).