Pathology database of tissue images for deep learning
Royal Philips and LabPON, the first clinical laboratory to move to 100% histopathology digital diagnosis, plans to create a digital database of massive aggregated sets of annotated pathology images and big data, utilising Philips IntelliSite Pathology Solution. The database will provide pathologists with a wealth of clinical information for the development of image analytics algorithms for computational pathology and pathology education, while promoting research and discovery to develop new insights for disease assessment, including cancer.
Deep learning algorithms have the potential to improve the objectivity and efficiency in tumour tissue diagnosis. In recent years, deep learning techniques for image analysis have become the state of the art in computer vision, and has surpassed human performance in a number of tasks. The challenge for executing deep learning techniques is having access to a database with sufficient high-volume and high-quality data from which to develop the algorithms.
As one of the largest pathology laboratories in The Netherlands, LabPON will contribute its repository of approximately 300,000 whole-slide images (WSI) created each year to the database. This will contain de-identified datasets of annotated cases that are manually commented by the pathologist, and will comprise a wide range of tissue and disease types, as well as other pertinent diagnostic information to facilitate deep learning.