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Article

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

1
Medmain Research, Medmain Inc., Fukuoka 810-0042, Fukuoka, Japan
2
Department of Clinical Laboratory, Sapporo Kosei General Hospital, 8-5 Kita-3-jo Higashi, Chuo-ku, Sapporo 060-0033, Hokkaido, Japan
3
Department of Surgical Pathology, Sapporo Kosei General Hospital, 8-5 Kita-3-jo Higashi, Chuo-ku, Sapporo 060-0033, Hokkaido, Japan
*
Author to whom correspondence should be addressed.
These authors contribute equally to this work.
Academic Editor: Samuel C. Mok
Cancers 2022, 14(5), 1159; https://doi.org/10.3390/cancers14051159
Received: 24 January 2022 / Revised: 18 February 2022 / Accepted: 22 February 2022 / Published: 24 February 2022
(This article belongs to the Collection Artificial Intelligence in Oncology)
In this pilot study, we aimed to investigate the use of deep learning for the classification of whole-slide images of liquid-based cytology specimens into neoplastic and non-neoplastic. To do so, we used a large training and test sets. Overall, the model achieved good classification performance in classifying whole-slide images, demonstrating the promising potential use of such models for aiding the screening processes for cervical cancer.
Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes. View Full-Text
Keywords: liquid-based cytology; deep learning; cervical screening; whole slide image liquid-based cytology; deep learning; cervical screening; whole slide image
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MDPI and ACS Style

Kanavati, F.; Hirose, N.; Ishii, T.; Fukuda, A.; Ichihara, S.; Tsuneki, M. A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers 2022, 14, 1159. https://doi.org/10.3390/cancers14051159

AMA Style

Kanavati F, Hirose N, Ishii T, Fukuda A, Ichihara S, Tsuneki M. A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers. 2022; 14(5):1159. https://doi.org/10.3390/cancers14051159

Chicago/Turabian Style

Kanavati, Fahdi, Naoki Hirose, Takahiro Ishii, Ayaka Fukuda, Shin Ichihara, and Masayuki Tsuneki. 2022. "A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images" Cancers 14, no. 5: 1159. https://doi.org/10.3390/cancers14051159

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