1D-DCGAN for Oversampling Minority Mitotic Patterns in HEp-2 Cell Images

Asaad Anaam, Mohammed A. Al-Masni, Akio Gofuku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a framework for oversampling the minority mitotic patterns of the HEp-2 cell images. The classification of mitotic vs. non-mitotic (interphase) cell patterns is important for validating the Indirect Immunofluorescence on Human Epithelial Type-2 cell-substrate (IIF HEp-2) protocol, which is the "gold standard"test for diagnosing autoimmune diseases. Typically, the mitotic cells appear in the HEp-2 specimen image in a significantly less number than the interphases. This causes difficulty in adopting deep learning approaches to classify mitotic vs. interphase patterns with such high imbalanced data. This work suggests using One-Dimensional Deep Convolutional Generative Adversarial Networks (1D-DCGAN) for oversampling the minority mitotic patterns in the feature space of the Deep Cross Residual Network (DCRNet) to cope with the data skewness problem. The results demonstrated that the proposed approach improves the classification performance over the UQ-SNP I3A Task-3 mitotic cell dataset with the advantage of using an end-to-end CNN classifier.

Original languageEnglish
Title of host publicationLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-174
Number of pages3
ISBN (Electronic)9781665419048
DOIs
Publication statusPublished - 2022
Event4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, Japan
Duration: Mar 7 2022Mar 9 2022

Publication series

NameLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies

Conference

Conference4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Country/TerritoryJapan
CityOsaka
Period3/7/223/9/22

Keywords

  • Computer-aided diagnosis (CADs)
  • Convolutional neural network (CNN)
  • HEp2 mitotic patterns
  • One-dimensional GAN
  • Oversampling

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Instrumentation
  • Education

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