Cervical Cell Detection Benchmark with Effective Feature Representation

Menglu Zhang, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


As deep convolutional neural networks have shown promising performance in medical image analysis, a number of deep learning based cervical cytology diagnosis methods were developed in recent years. Most studies have achieved available performance in cell classification or segmentation, however, there still exists some challenges for effective screening. Cervical cell detection is a more significant task in cytology diagnosis for cancers. In this paper, we propose a detection framework with effective feature representation for automatic cervical cytology analysis. We employ elastic transformation and a channel and spacial attention module to obtain a more powerful feature extractor. The experimental results demonstrate the efficiency and accuracy improved by our effective feature representation.

Original languageEnglish
Title of host publicationCognitive Systems and Signal Processing - 5th International Conference, ICCSIP 2020, Revised Selected Papers
EditorsFuchun Sun, Huaping Liu, Bin Fang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789811623356
Publication statusPublished - 2021
Externally publishedYes
Event5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020 - Zhuhai, China
Duration: 25 Dec 202027 Dec 2020

Publication series

NameCommunications in Computer and Information Science
Volume1397 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020


  • Cervical cytology diagnosis
  • Detection framework
  • Feature representation

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


Dive into the research topics of 'Cervical Cell Detection Benchmark with Effective Feature Representation'. Together they form a unique fingerprint.

Cite this