The Digital Pathology Project aims to collect 10,000 pathology slides from up to 30 different cancer types, and to create infrastructure for automated clinical data handling, online slide annotation and machine learning support. The rich dataset of annotated pathology slides will be shared with project partners. Using convolutional neural network-based machine learning, the project aims to create an automated tumour identification and mapping tool to enhance and accelerate diagnostics.
This project interfaces with OvCare, the Personalized OncoGenomics Program, the Tissue Tumor Repository, and the Canadian Digital Technology Supercluster (CDTS) Dermatology Point of Care Intelligent Network, a medical imaging network, powered by artificial intelligence, that aims to enhance the diagnosis, treatment and management of skin cancer patients in the province of British Columbia and beyond.
