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

  • Jiahao Lu
  • Nataša Sladoje
  • Christina Runow Stark
  • Eva Darai Ramqvist
  • Jan-Michaél Hirsch
  • Joakim LindbladEmail author
Conference paper
  • 154 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12132)

Abstract

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).

Keywords

CNN Whole slide imaging Big data Cytology Detection Focus selection Classification 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Centre for Image Analysis, Department of ITUppsala UniversityUppsalaSweden
  2. 2.Department of Orofacial Medicine at SödersjukhusetFolktandvården Stockholms Län ABStockholmSweden
  3. 3.Department of Clinical Pathology and CytologyKarolinska University HospitalStockholmSweden
  4. 4.Department of Surgical sciencesUppsala UniversityUppsalaSweden

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