A Deep Learning Based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Lu, Jiahao (author): Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion

Sladoje, Natasa (author): Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion

Runow Stark, Christina (author)

Darai Ramqvist, Eva (author)

Hirsch, Jan-Michaél (author)

Lindblad, Joakim (author): Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion

(creator_code:org_t)

2020-06-17
2020
English.
In: Image Analysis and Recognition. - Cham : Springer International Publishing. - 9783030505158 - 9783030505165 ; , s. 249-261

Related links:: https://www.guide2re...; show more...; http://arxiv.org/pdf...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

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).

By the author/editor: Lu, Jiahao; Sladoje, Natasa; Runow Stark, Chr ...; Darai Ramqvist, ...; Hirsch, Jan-Mich ...; Lindblad, Joakim

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Medical Engineer ...; and Medical Image Pr ...

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