ArticleFrontiers in bioinformatics2023
Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Deep Learning and Attention Mechanism-based Prediction of Vaginal Invasion in Early-Stage Cervical CancerCurrent medical imaging · 2026Article
- Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Automated reporting of cervical biopsies using artificial intelligence.PLOS digital health · 2024Article
- Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.PloS one · 2024Article
- Ensemble Learning to Categorize the Bethesda System of Reporting of Cervical Cytology.Journal of cytologyArticle
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer (CC) is the fourth most common malignant tumor among women worldwide. Constructing a high-accuracy deep convolutional neural network (DCNN) for cervical cancer screening and diagnosis is important for the successful prevention of cervical cancer. In this work, we proposed a robust DCNN for cervical cancer screening using whole-slide images (WSI) of ThinPrep cytologic test (TCT) slides from 211 cervical cancer and 189 normal patients. We used an active learning strategy to improve the efficiency and accuracy of image labeling. The sensitivity, specificity, and accuracy of the best model were 96.21%, 98.95%, and 97.5% for CC patient identification respectively. Our results also demonstrated that the active learning strategy was superior to the traditional supervised learning strategy in cost reduction and improvement of image labeling quality. The related data and source code are freely available at https://github.com/hqyone/cancer_rcnn.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.