Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images.

ABSTRACT: Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 wer

SUBMITTER: Kurita Y 

PROVIDER: S-EPMC10194854 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature