Automated Screening of Precancerous Cervical Cells Through Contrastive Self-Supervised Learning

Jaewoo Chun, Ando Yu, Seokhwan Ko, Gunoh Chong, Jiyoung Park, Hyungsoo Han, Nora Jeeyoung Park, Junghwan Cho

Research output: Contribution to journalArticlepeer-review

Abstract

Cervical cancer is a significant health challenge, yet it can be effectively prevented through early detection. Cytology-based screening is critical for identifying cancerous and precancerous lesions; however, the process is labor-intensive and reliant on trained experts to scan through hundreds of thousands of mostly normal cells. To address these challenges, we propose a novel distribution-augmented approach using contrastive self-supervised learning for detecting abnormal squamous cervical cells from cytological images. Our method utilizes color augmentations to enhance the model’s ability to differentiate between normal and high-grade precancerous cells; specifically, high-grade squamous intraepithelial lesions (HSILs) and atypical squamous cells–cannot exclude HSIL (ASC-H). Our model was trained exclusively on normal cervical cell images and achieved high diagnostic accuracy, demonstrating robustness against color distribution shifts. We employed kernel density estimation (KDE) to assess cell type distributions, further facilitating the identification of abnormalities. Our results indicate that our approach improves screening accuracy and reduces the workload for cytopathologists, contributing to more efficient cervical cancer screening programs.

Original languageEnglish
Article number1565
JournalLife
Volume14
Issue number12
DOIs
StatePublished - Dec 2024

Keywords

  • cervical cancer
  • cytology-based screening
  • distribution-augmented contrastive learning
  • precancerous cells
  • self-supervised learning

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