期刊
JOURNAL OF DIGITAL IMAGING
卷 29, 期 4, 页码 496-506出版社
SPRINGER
DOI: 10.1007/s10278-016-9873-1
关键词
Digital pathology; Medical image analysis; Computer vision; Region of interest; Whole slide imaging
资金
- National Cancer Institute of the National Institutes of Health [R01 CA172343, R01 CA140560, KO5 CA104699]
- Scientific and Technological Research Council of Turkey [113E602]
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.
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