ArticleDiagnostics (Basel, Switzerland)2020
Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 2025Article
- Cleaning and Harmonizing Medical Image Data for Reliable AI: Lessons Learned from Longitudinal Oral Cancer Natural History Study Data.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Optical imaging for screening and early cancer diagnosis in low-resource settings.Nature reviews bioengineering · 2024Article
- Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.Bioengineering (Basel, Switzerland) · 2023Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- Data Characterization for Reliable AI in Medicine.Recent trends in image processing and pattern recognition : 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 01-02, 2022, revised selected papers. International Conference on Recent Trends in Image Processing and... · 2023Article
- Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer.Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. MILLanD (Workshop) (1st : 2022 : Singapore) · 2022Article
- A Case Study of Multiple Maintenance Efficacy in Gynaecological Surgery Assessed by Deep Learning.Computational and mathematical methods in medicine · 2022Article
- Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks.PloS one · 2022Article
- Addressing cervical cancer screening disparities through advances in artificial intelligence and nanotechnologies for cellular profiling.Biophysics reviews · 2021Review
- Quality Assessment of Endoscopic Forceps Biopsy Samples under Magnifying Narrow Band Imaging for Histological Diagnosis of Cervical Intraepithelial Neoplasia: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2021Article
- Deep Metric Learning for Cervical Image Classification.IEEE access : practical innovations, open solutions · 2021Article
- Improvement method for cervical cancer detection: A comparative analysis.Oncology research · 2021Review
- Deep Learning in Selected Cancers' Image Analysis-A Survey.Journal of imaging · 2020Review
- Uterine Cervical Neoplasm Diagnosed by Flexible Magnifying Endoscopy with Narrow Band Imaging.Diagnostics (Basel, Switzerland) · 2020Article
- Analysis of digital noise and reduction methods on classifiers used in automated visual evaluation in cervical cancer screening.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- 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
9 authors.
Funding
Abstract
Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted by the National Cancer Institute (NCI) and has been shown to accurately identify cervices with early stages of cervical neoplasia for clinical evaluation and treatment. The algorithm processes images of the uterine cervix taken with a digital camera and alerts the user if the woman is a candidate for further evaluation. This requires that the algorithm be presented with images of the cervix, which is the object of interest, of acceptable quality, i.e., in sharp focus, with good illumination, without shadows or other occlusions, and showing the entire squamo-columnar transformation zone. Our prior work has addressed some of these constraints to help discard images that do not meet these criteria. In this work, we present a novel algorithm that determines that the image contains the cervix to a sufficient extent. Non-cervix or other inadequate images could lead to suboptimal or wrong results. Manual removal of such images is labor intensive and time-consuming, particularly in working with large retrospective collections acquired with inadequate quality control. In this work, we present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. The ensemble method combined the assessment of three deep learning architectures, RetinaNet, Deep SVDD, and a customized CNN (Convolutional Neural Network), each using a different strategy to arrive at its decision, i.e., object detection, one-class classification, and binary classification. We examined the performance of each individual architecture and an ensemble of all three architectures. An average accuracy and F-1 score of 91.6% and 0.890, respectively, were achieved on a separate test dataset consisting of more than 30,000 smartphone-captured images.
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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.