title: An Interactive Learning Framework for Scalable Classification of Pathology Images.
abstract: Recent advances in microscopy imaging and genomics have created an explosion of patient data in the pathology domain. Whole-slide images (WSIs) of tissues can now capture disease processes as they unfold in high resolution, recording the visual cues that have been the basis of pathologic diagnosis for over a century. Each WSI contains billions of pixels and up to a million or more microanatomic objects whose appearances hold important prognostic information. Computational image analysis enables the mining of massive WSI datasets to extract quantitative morphologic features describing the visual qualities of patient tissues. When combined with genomic and clinical variables, this quantitative information provides scientists and clinicians with insights into disease biology and patient outcomes. To facilitate interaction with this rich resource, we have developed a web-based machine-learning framework that enables users to rapidly build classifiers using an intuitive active learning process that minimizes data labeling effort. In this paper we describe the architecture and design of this system, and demonstrate its effectiveness through quantification of glioma brain tumors.
authors: Department of Computer Science and Mathematics, Emory University, Emory University School of Medicine, Atlanta, GA 30322.
authors: Michael
authors: M
authors: Nalisnik
authors: Department of Neurology, Emory University, Emory University School of Medicine, Atlanta, GA 30322; Winship Cancer Institute, Emory University, Emory University School of Medicine, Atlanta, GA 30322.
authors: David A
authors: DA
authors: Gutman
authors: Departments of Biomedical Informatics, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322.
authors: Jun
authors: J
authors: Kong
authors: Departments of Biomedical Informatics, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322; Department of Biomedical Engineering, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322; Winship Cancer Institute, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322.
authors: Lee Ad
authors: LA
authors: Cooper
keywords: biomedical image processing; interactive systems; machine learning; pathology