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RESEARCH ARTICLE Computational Pathology to Discriminate Benign from Malignant Intraductal Proliferations of the Breast Fei Dong1,2., Humayun Irshad3., Eun-Yeong Oh3, Melinda F. Lerwill1, Elena F.
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Document Date: 2014-12-19 16:49:25


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File Size: 1,55 MB

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Boston / /

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PLOS ONE / Creative Commons / Philips / /

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Netherlands / Fiji / United States / /

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Facility

Massachusetts General Hospital / MGH facility / Women’s Hospital / National Library of Medicine / University of Torino / Beth Israel Deaconess Medical Center / /

IndustryTerm

image processing code / digital pathology tools / real-time decision support / computational pathology algorithms / computational tools / laboratory protocol / energy / requisite hardware / investigational tools / image processing / segmentation algorithm / slide imaging / /

MedicalCondition

DCIS / UDH Model The top-performing DCIS / intermediate-and-low grade DCIS / proliferative breast disease / ductal carcinoma / translational cancer / high grade DCIS / UDH DCIS / breast cancer / subsequent breast cancer / both DCIS / high nuclear grade DCIS / low grade DCIS / intermediate-and-high grade DCIS / Top-Performing DCIS / invasive carcinoma / Breast Intraductal Proliferations DCIS / intermediate grade DCIS / /

MedicalTreatment

breast radiation therapy / mastectomy / lumpectomy / /

NaturalFeature

Hematoxylin channel / /

Organization

Harvard Medical School / Department of Pathology / Beth Israel Deaconess Medical Center / Women’s Hospital / Massachusetts General Hospital / University of Torino / Partners Human Research Committee / /

Person

Anna Sapino / Andrew H. Beck / Beth Israel / /

Position

original author / Editor / Academic Editor / representative / /

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L1 / Philips Scanner / scanned using a Philips Scanner / /

ProvinceOrState

Massachusetts / /

PublishedMedium

PLoS ONE / /

Technology

computational pathology algorithms / radiation / standardized laboratory protocol / machine learning / image processing / digital photography / segmentation algorithm / /

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http /

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