Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.
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 releva...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 4; pp. 496 - 507 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116774808&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116774808 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2016 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116774808 116774808 116774808 10.1007/s10278-016-9873-1 116774808 ppf: 496 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study. aug: au: Mercan, Ezgi Aksoy, Selim Shapiro, Linda Weaver, Donald Brunyé, Tad Elmore, Joann affil: Department of Computer Science & Engineering, Paul G. Allen Center for Computing, University of Washington, 185 Stevens Way Seattle 98195 USA sug: subj: Digital Imaging Pathology, Clinical Diagnostic Imaging Biopsy Human Comparative Studies ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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