Automatic and Manual Proliferation Rate Estimation from Digital Pathology Images

Abstract

Digital pathology is a major revolution in pathology and is changing the clinical routine for pathologists. We work on providing a computer aided diagnosis system that automatically and robustly provides the pathologist with a second opinion for many diagnosis tasks. However, inter-observer variability prevents thorough validation of any proposed technique for any specific problems. In this work, we study the variability and reliability of proliferation rate estimation from digital pathology images for breast cancer proliferation rate estimation. We also study the robustness of our recently proposed method CAD system for PRE estimation. Three statistical significance tests showed that our automated CAD system was as reliable as the expert pathologist in both brown and blue nuclei estimation on a dataset of 100 images.