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A Deep Learning Bas...
A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
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- Lu, Jiahao (author)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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- Sladoje, Natasa (author)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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Runow Stark, Christina (author)
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Darai Ramqvist, Eva (author)
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Hirsch, Jan-Michaél (author)
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- Lindblad, Joakim (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion
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(creator_code:org_t)
- 2020-06-17
- 2020
- English.
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In: Image Analysis and Recognition. - Cham : Springer International Publishing. - 9783030505158 - 9783030505165 ; , s. 249-261
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Abstract
Subject headings
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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).
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- CNN
- Whole slide imaging
- Big data
- Cytology
- Detection
- Focus selection
- Classification
- Datoriserad bildbehandling
- Computerized Image Processing
Publication and Content Type
- ref (subject category)
- kon (subject category)
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