title: Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.
abstract: Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.
authors.affiliation: Department of Computer Science & Engineering, Paul G. Allen Center for Computing, University of Washington, 185 Stevens Way, Seattle, WA, 98195, USA. ezgi@cs.washington.edu.
authors.forename: Ezgi
authors.initials: E
authors.lastname: Mercan
authors.affiliation: Department of Computer Engineering, Bilkent University, Bilkent, 06800, Ankara, Turkey.
authors.forename: Selim
authors.initials: S
authors.lastname: Aksoy
authors.affiliation: Department of Computer Science & Engineering, Paul G. Allen Center for Computing, University of Washington, 185 Stevens Way, Seattle, WA, 98195, USA.
authors.forename: Linda G
authors.initials: LG
authors.lastname: Shapiro
authors.affiliation: Department of Pathology, University of Vermont, Burlington, VT, 05405, USA.
authors.forename: Donald L
authors.initials: DL
authors.lastname: Weaver
authors.affiliation: Department of Psychology, Tufts University, Medford, MA, 02155, USA.
authors.forename: Tad T
authors.initials: TT
authors.lastname: Brunyé
authors.affiliation: Department of Medicine, University of Washington, Seattle, WA, 98195, USA.
authors.forename: Joann G
authors.initials: JG
authors.lastname: Elmore
mesh_terms: D001706:Biopsy; D001940:Breast / Q000000981:diagnostic imaging* / Q000473:pathology*; D003657:Decision Making; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D019300:Medical Errors; D000072142:Pathologists
keywords: Computer vision; Digital pathology; Medical image analysis; Region of interest; Whole slide imaging