INNOVATIONS

Description

Colorectal cancer (CRC) is one of the top leading causes of cancer deaths around the world. Although colonoscopy is an effective tool for CRC screening, two major difficulties faced by endoscopists are missed polyps and misclassified polyps. This proposal aims to develop a real-time computer-aided system (CAS) for polyp detection and polyp type classification during colonoscopy. Existing CAS only perform either polyp detection (i.e. with or without polyps) or polyp type classification (neoplastic or non-neoplastic). In this study, a system that can perform both tasks simultaneously will be studied, i.e. by formulating the problem as a three-class image classification task (non-polyp, non-neoplastic polyp and neoplastic polyp). A set of unstructured endoscopic images, with clean labels, will be collected. Advanced machine learning technologies, including deep learning and transfer learning techniques with a novel architecture, will be implemented and evaluated on the set of endoscopic images. It is anticipated that the proposed CAS will display the diagnostic results of each frame in real time during colonoscopy to aid decision making during CRC screening.