An Interactive Learning Framework for Scalable Classification of Pathology Images

• Type of Material: Conference Paper
• Authors: Michael Nalisnik, Emory University; Jun Kong, Emory University; David Gutman, Emory University; Lee Cooper, Emory University
• Language: English
• Date: 2015-12-28
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 IEEE.
• Final Published Version (URL): http://dx.doi.org/10.1109/BigData.2015.7363841
• Title of Journal or Parent Work: Proceedings 2015 IEEE International Conference On Big Data
• Conference or Event Name: IEEE International Conference on Big Data
• Volume: 2015
• Start Page: 928
• End Page: 935
• Grant/Funding Information: This work was funded by the National Institutes of Health, National Library of Medicine Career Development Award in Biomedical Informatics (K22LM011576), National Cancer Institute Informatics Technology for Cancer Research Program (U24CA194362) and National Cancer Institute Career Development Award (K25CA181503).
• Keywords: machine learning; biomedical image processing; Computer Science, Information Systems; Engineering; INFORMATICS; Technology; Engineering, Electrical & Electronic; pathology; Computer Science; QUANTITATIVE-ANALYSIS; Science & Technology; WHOLE-SLIDE IMAGES; interactive systems
• Research Categories: Health Sciences, Pathology; Computer Science; Biology, Bioinformatics

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.