The Development of Locoregional Therapies as a Strategy for Reducing Cervical Cancer Mortality in Low to Middle Income Countries.

Chituku, Tsitsi; Jones, Susan L.; Phillips, Roger M.

doi:10.22541/au.164873341.16568307/v1

Cited by 2 publications

(2 citation statements)

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“…For early diagnosis of cervical cancer, its screening using cervical cytology specimens is performed based on the Bethesda System [ 21 ]. However, compared to high-income countries, cytology is less extensively employed in low- and middle-income countries due to a lack of healthcare infrastructure and a paucity of cytologists or cytopathologists [ 18 – 20 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Cervical cancer is the fourth most frequent cancer in women worldwide, with an estimated 604,000 new cases and 342,000 deaths in 2020 [16], with low-and middle-income countries accounting for approximately 90% of the cases and deaths [17][18][19][20]. For early diagnosis of cervical cancer, its screening using cervical cytology specimens is performed based on the Bethesda System [21].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Section: Introductionmentioning

confidence: 99%