ArticleNature communications2021
Robust whole slide image analysis for cervical cancer screening using deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
70 citing papers in PubMed, 1 synthesis or guideline pooled it, 172 citations in OpenAlex.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Urban-rural disparities in HPV prevalence and genotype distribution: a large-scale cervical cancer screening study in Xi'an, China.BMC infectious diseases · 2026Article
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification.iScience · 2026Article
- Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trial.NPJ digital medicine · 2026Article
- Robust Cell-Level Classification for Liquid-Based Cervical Cytology Using Deep Transfer Learning: A Multi-Source Study Addressing Scanner-Induced Domain Shifts.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- UniCAS: A foundation model for cervical cytology screening.Cell reports. Medicine · 2026Article
- Pre-analytical reporting in AI-assisted cervical cytology: a scoping review of data acquisition documentation.Frontiers in artificial intelligence · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- A computation-efficient network with feature aggregation for cancer subtype classification on histopathological images.Engineering applications of artificial intelligence · 2025Article
- An integrated platform for liquid handling and cell imaging in life science applications.Nature communications · 2025Article
- Computer-assisted quantitative framework for whole slide cervical image grading driven by time series features.Scientific reports · 2025Article
- AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscope.Nature communications · 2025Article
- Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning.Nature communications · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.BMC medical informatics and decision making · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.
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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.