Convolutional Neural Networks for False Positive Reduction of Automatically Detected Cilia in Low Magnification TEM Images

  • Anindya Gupta
  • Amit Suveer
  • Joakim Lindblad
  • Anca Dragomir
  • Ida-Maria Sintorn
  • Nataša Sladoje
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)

Abstract

Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

Keywords

Convolutional neural network Primary Ciliary Dyskinesia Template maching Transmission electron microscopy 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anindya Gupta
    • 1
  • Amit Suveer
    • 2
  • Joakim Lindblad
    • 2
    • 3
  • Anca Dragomir
    • 4
  • Ida-Maria Sintorn
    • 2
    • 5
  • Nataša Sladoje
    • 2
    • 3
  1. 1.T.J. Seebeck Department of ElectronicsTallinn University of TechnologyTallinEstonia
  2. 2.Department of IT, Centre for Image AnalysisUppsala UniversityUppsalaSweden
  3. 3.Mathematical InstituteSerbian Academy of Sciences and ArtsBelgradeSerbia
  4. 4.Department of Surgical PathologyUppsala University HospitalUppsalaSweden
  5. 5.Vironova ABStockholmSweden

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