ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2022
Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
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Who cites it
15 citing papers in PubMed.
- From HPV infection to cervical precancer: Molecular mechanisms, immune microenvironment and emerging technologies.Cellular and molecular life sciences : CMLS · 2026Review
- AI-Enhanced Point-of-Care Diagnostics for Infectious Diseases in Resource-Limited Settings: A Scoping Review.Tropical medicine & international health : TM & IH · 2026Article
- Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.Discover oncology · 2025Review
- A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer.Clinical Medicine Insights. Oncology · 2025Review
- Automated analysis of scattering-based light sheet microscopy images of anal squamous intraepithelial lesions.Biomedical optics express · 2024Article
- Multiscale Optical Imaging Fusion for Cervical Precancer Diagnosis: Integrating Widefield Colposcopy and High-Resolution Endomicroscopy.IEEE transactions on bio-medical engineering · 2024Article
- Emerging trends and hotspots in cervical intraepithelial neoplasia research from 2013 to 2023: A bibliometric analysis.Heliyon · 2024Article
- Rapid, high-resolution, non-destructive assessments of metabolic and morphological homogeneity uniquely identify high-grade cervical precancerous lesions.bioRxiv : the preprint server for biology · 2024Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Optical imaging for screening and early cancer diagnosis in low-resource settings.Nature reviews bioengineering · 2024Article
- Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy.Scientific reports · 2023Article
- Cervical Cancer Detection Techniques: A Chronological Review.Diagnostics (Basel, Switzerland) · 2023Review
- Multimodal optical imaging with real-time projection of cancer risk and biopsy guidance maps for early oral cancer diagnosis and treatment.Journal of biomedical optics · 2023Article
- Development of a multimodal mobile colposcope for real-time cervical cancer detection.Biomedical optics express · 2022Article
- Improvement method for cervical cancer detection: A comparative analysis.Oncology research · 2021Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94.
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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.