Transfer learning based classification of cervical cancer immunohistochemistry images

C. Li, D. Xue, X. Zhou, J. Zhang, H. Zhang, Y. Yao, F. Kong, L. Zhang, H. Sun

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

26 Scopus citations

Abstract

Cervical cancer is the fourth leading cause of cancer-related deaths. It is very important to make the precise diagnosis for the early stage of cervical cancer. In recent years, transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in cervical histopathology image classification becomes a new research domain. In this paper, we propose a transfer learning framework of Inception-V3 network to classify well, moderately and poorly differentiated cervical histopathology images, which are stained using immunohistochemistry methods. In this framework, an Inception-V3 based transfer learning structure is first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from the structure. Finally, the extracted features are designed for the final classification. In the experiment, a practical images stained by AQP, HIF and VEGF approaches are applied to test the proposed transfer learning network, and an average accuracy of 77.3% is finally achieved.

Original languageEnglish
Title of host publicationISICDM 2019 - Conference Proceedings
Subtitle of host publication3rd International Symposium on Image Computing and Digital Medicine
Pages102-106
Number of pages5
ISBN (Electronic)9781450372626
DOIs
StatePublished - 24 Aug 2019
Event3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019 - Xi'an, China
Duration: 24 Aug 201926 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019
Country/TerritoryChina
CityXi'an
Period24/08/1926/08/19

Keywords

  • Cervical cancer
  • Classification
  • Histopathology image
  • Immunohistochemistry staining
  • Inception-V3
  • Transfer learning

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