Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image

AMIA Annu Symp Proc. 2020 Mar 4:2019:820-827. eCollection 2019.

Abstract

Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting deep hierarchical features by fine-tuning convolution neural networks, as well as a novel graph-based cell detection approach for cellular level evaluation. The results show that the proposed pipeline can classify images of both single cell and overlapping cells. The VGG-19 model is found to be the best at classifying the cervical cytology patch data with 95 % accuracy under precision-recall curve.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Intramural

MeSH terms

  • Cervix Uteri / cytology
  • Datasets as Topic
  • Deep Learning*
  • Female
  • Humans
  • Neural Networks, Computer
  • Papanicolaou Test*
  • ROC Curve
  • Uterine Cervical Neoplasms / pathology*
  • Vaginal Smears / methods*