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

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 d...

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Veröffentlicht in:PloS one 2023-05, Vol.18 (5), p.e0285996-e0285996
Hauptverfasser: Kurita, Yuki, Meguro, Shiori, Tsuyama, Naoko, Kosugi, Isao, Enomoto, Yasunori, Kawasaki, Hideya, Uemura, Takashi, Kimura, Michio, Iwashita, Toshihide
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container_title PloS one
container_volume 18
creator Kurita, Yuki
Meguro, Shiori
Tsuyama, Naoko
Kosugi, Isao
Enomoto, Yasunori
Kawasaki, Hideya
Uemura, Takashi
Kimura, Michio
Iwashita, Toshihide
description 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 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.
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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 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. 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deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images</title><author>Kurita, Yuki ; Meguro, Shiori ; Tsuyama, Naoko ; Kosugi, Isao ; Enomoto, Yasunori ; Kawasaki, Hideya ; Uemura, Takashi ; Kimura, Michio ; Iwashita, Toshihide</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c693t-ad6b4cb83c08a73e17f50e415763ff43c6c26b3d24fecd26defe10a5970cf8933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Cancer</topic><topic>Cancer screening</topic><topic>Cellular biology</topic><topic>Cervical cancer</topic><topic>Clinical medicine</topic><topic>Computational linguistics</topic><topic>Computer and Information Sciences</topic><topic>Cytology</topic><topic>Deep Learning</topic><topic>Diagnosis</topic><topic>Early Detection of 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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 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. 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source Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry
subjects Analysis
Biology and Life Sciences
Cancer
Cancer screening
Cellular biology
Cervical cancer
Clinical medicine
Computational linguistics
Computer and Information Sciences
Cytology
Deep Learning
Diagnosis
Early Detection of Cancer
Female
Histopathology
Humans
Image classification
Language processing
Lesions
Low income groups
Machine learning
Medical diagnosis
Medical imaging
Medical screening
Medicine and Health Sciences
Model accuracy
Model testing
Modelling
Natural language interfaces
People and Places
Reproducibility of Results
Research and Analysis Methods
Semi-supervised learning
Social Sciences
Squamous Intraepithelial Lesions
Students
Supervised Machine Learning
Technology application
Uterine Cervical Neoplasms - diagnostic imaging
Uterine Cervical Neoplasms - pathology
title Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images
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