Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer.
Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis.
Dept. of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Sonal
S
Kothari
Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
John H
JH
Phan
Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
Andrew N
AN
Young
Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
May D
MD
Wang