About: Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach

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scholarly article by Jason W Wei et al published 2019 in Journal of pathology informatics

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rdf:type	Item
description	wetenschappelijk artikel (nl) im Jahr 2019 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована у 2019 (uk) scholarly article by Jason W Wei et al published 2019 in Journal of pathology informatics (en)
publication date	wds:Q64111147-E3185C54-A299-475E-AE58-6536FDC82E1A
publication date	2019-01-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q64111147-2CEED5CB-BD4F-4CD3-A913-9571E009A401
language of work or name	English
cites work	wds:Q64111147-02724EEF-734B-42FD-9B7F-4D886C7CA03D wds:Q64111147-0F4C308A-5251-46D2-A554-45EFC91DBC3D wds:Q64111147-15918CC9-0F8D-4393-9463-D886CC28BB00 wds:Q64111147-18AE3A51-32BC-484B-A54D-C90CF687167C wds:Q64111147-2AF9406B-A2DE-4F2A-97A9-7402C6A55EA6 wds:Q64111147-2B803267-8C29-4EAB-BCC1-3D8B1D4915CD wds:Q64111147-323E962D-B5CC-403A-8BBA-A1D304E62E57 wds:Q64111147-4C0B1FED-F7D5-4E19-924D-DFEB6051AD45 wds:Q64111147-5654ED88-DF12-445D-A5E7-FBFB731480E3 wds:Q64111147-63D847D2-8B92-4E76-AB61-9D5052AB30F2 wds:Q64111147-6F4985B5-2560-4BFD-A03B-58F75A94F520 wds:Q64111147-7338BBCD-F61A-4792-AE73-76CBE118FBC1 wds:Q64111147-75594D3D-0B53-4AFF-B0AA-79FFA533AD3D wds:Q64111147-79F799CA-9862-423C-B556-0BF39FE919B4 wds:Q64111147-8115055D-A314-4EAC-9CBF-02FF7A31172A wds:Q64111147-920FC8CE-C8D0-4CA4-B764-1BB4133A6D35 wds:Q64111147-A8F2DF4C-F786-4707-A1DF-6E8CD01055AE wds:Q64111147-ADA89F12-F12A-4CB8-89F8-7FB08BB6BD98 wds:Q64111147-B24498A7-4DAA-4DAE-91AD-784B4DE6200C wds:Q64111147-B5710AC1-2A7F-47F2-9F30-889CDB352C73 wds:Q64111147-DDCE3036-9289-43CD-94BB-94A8CE1E9C5D wds:Q64111147-E1DE7D48-C37E-45E8-BCF7-3D166E91B185 wds:Q64111147-E84999F5-235F-47CF-B496-F5BF88A484C9 wds:Q64111147-E9D57235-1DE5-4046-AB3F-9FFF12653CD1 wds:Q64111147-EF2F99B3-DFEE-417E-8988-DE3CC9942FE3 wds:Q64111147-F382CA8A-884D-4CBD-9CF9-EEA7D59AE61D
cites work	Gluten, major histocompatibility complex, and the small intestine. A molecular and immunobiologic approach to the spectrum of gluten sensitivity ('celiac sprue') Current approaches to diagnosis and treatment of celiac disease: an evolving spectrum Comparison of the interobserver reproducibility with different histologic criteria used in celiac disease Celiac Disease High prevalence of celiac disease in apparently healthy blood donors suggests a high prevalence of undiagnosed celiac disease in the Dutch population Classification and management of refractory coeliac disease Seroprevalence, correlates, and characteristics of undetected coeliac disease in England Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs ACG clinical guidelines: diagnosis and management of celiac disease The prevalence of celiac disease in average-risk and at-risk Western European populations: a systematic review Morbidity and mortality among older individuals with undiagnosed celiac disease Deep learning Histopathology of celiac disease Increased prevalence and mortality in undiagnosed celiac disease The prevalence of celiac disease in Europe: results of a centralized, international mass screening project. Variability in small bowel histopathology reporting between different pathology practice settings: impact on the diagnosis of coeliac disease Reproducibility of the histological diagnosis of celiac disease. Diagnosis of coeliac disease An approach to duodenal biopsies Validation of morphometric analyses of small-intestinal biopsy readouts in celiac disease Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method. Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis Routine duodenal biopsies to diagnose celiac disease Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans
author name string	wds:Q64111147-3E4A8765-253D-4558-BF2F-B78B8F3C2EC2 wds:Q64111147-B0685F55-B2E7-4A13-88D0-EF9B84CBEAB4 wds:Q64111147-C088CC16-C83D-4BD9-8DB6-2AC29DDCED91 wds:Q64111147-C1F1E0C2-7B19-43A1-8FFD-BF42603197A3 wds:Q64111147-CDA81AA1-E4B5-4615-914C-9214B96B6E6D wds:Q64111147-F335B98D-6FC4-42E2-8F47-5E04610E05FC
author name string	Bing Ren Arief A Suriawinata Christopher R Jackson Saeed Hassanpour Jason W Wei Jerry W Wei
rdfs:label	Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (en) Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (nl)
skos:prefLabel	Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (en) Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (nl)
name	Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (en) Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (nl)
title	wds:Q64111147-8DA4BC98-A46E-4536-92EF-48213F3E77C1
title	Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach (en)
page(s)	wds:Q64111147-F6732914-3F52-4012-8547-84522F113297
page(s)	7
instance of	wds:Q64111147-49A1E05D-75EC-4373-A381-7C9981A990BF
instance of	scholarly article
main subject	wds:Q64111147-33AB3989-13D6-4317-BD4E-BDACDADACAAF wds:Q64111147-CA77184F-B25F-436B-A9EA-6F26236D7D99
main subject	deep learning digital pathology
PubMed ID	wds:Q64111147-5BA5722A-F6A9-4D81-A158-7410659065C1
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/30984467
PubMed ID	30984467
published in	wds:Q64111147-689888E1-5E6E-47A2-B576-C20C7C43DDAA
published in	Journal of pathology informatics
issue	wds:Q64111147-5A26C495-3A45-4D5B-8197-A7843B80555D
volume	wds:Q64111147-05D1F602-3B44-4162-899A-8ACC3BCF4DF0
issue	1
volume	10
DOI	wds:Q64111147-0AD6AC45-F0F3-41CF-BA68-E29ADF1B314A
DOI	http://dx.doi.org/10.4103/JPI.JPI_87_18

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