Automated Nuclei Detection in Serous Effusion Cytology with Stacked Sparse Autoencoders

BAYKAL KABLAN E., DOĞAN H., ERCİN M. E., ERSÖZ Ş., EKİNCİ M.

26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2 - 05 May 2018 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2018.8404315
  • City: İzmir
  • Country: Turkey
  • Karadeniz Technical University Affiliated: Yes

Abstract

Serous effusion is frequently encountered specimen type in (cyto)pathological assessment. However, this assessment is time-consuming and leads to variability among pathologists. The cell nuclei is seen as the corner stone for diagnostic purposes in automatic analysis of cytopathological images. In this paper, a stacked sparse autoencoder (SSAE) is proposed for nuclei detection in serous effusion cytology. SSAE is an unsupervised deep learning method which learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. High-level features, obtained via the autoencoder, are then subsequently fed to a softmax classifier which categorizes each patch as nuclei or non-nuclei. With a detection rate of 98.3% based on images of serous effusion cytology, proposed method shows good performance.