Search: onr:"swepub:oai:DiVA.org:uu-427210" >
A Deep Learning Bas...
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Lu, JiahaoUppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
(author)
A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
- Article/chapterEnglish2020
Publisher, publication year, extent ...
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2020-06-17
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Cham :Springer International Publishing,2020
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:uu-427210
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-427210URI
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https://doi.org/10.1007/978-3-030-50516-5_22DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is made available as open source (https://github.com/MIDA-group/OralScreen).
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Sladoje, NatasaUppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion(Swepub:uu)namat934
(author)
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Runow Stark, Christina
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Darai Ramqvist, Eva
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Hirsch, Jan-Michaél
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Lindblad, JoakimUppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion(Swepub:uu)joali534
(author)
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Uppsala universitetAvdelningen för visuell information och interaktion
(creator_code:org_t)
Related titles
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In:Image Analysis and RecognitionCham : Springer International Publishing, s. 249-26197830305051589783030505165
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