Digital pathology annotation data for improved deep neural network classification
Edward Kim, SaiLakshmiDeepika Mente, Andrew Keenan, Vijay Gehlot · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
In the field of digital pathology, there is an explosive amount of imaging data being generated. Thus, there is an ever growing need to create assistive or automatic methods to analyze collections of images for screening and classification. Machine learning, specifically deep learning algorithms, developed for digital pathology have the potential to assist in this way. Deep learning architectures have demonstrated great success over existing classification models but require massive amounts of labeled training data that either doesn’t exist or are cost and time prohibitive to obtain. In this project, we present a framework for representing, collecting, validating, and utilizing cytopathology features for improved neural network classification.