ReviewInternational journal of molecular sciences2019
A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.
Review in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- 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
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- CPSMI2025: A curated dataset of conventional Pap smear microscopy images for deep learning-based cervical cancer screening.Data in brief · 2025Article
- Contemporary Status and Frontiers of Cervical Cancer Screening in the United States.Cancer prevention research (Philadelphia, Pa.) · 2025Review
- A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings.Frontiers in medical technology · 2025Article
- Automated analysis of digital medical images in cervical cancer screening: A systematic review.medRxiv : the preprint server for health sciences · 2024Article
- CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.Heliyon · 2024Article
- Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.PloS one · 2024Article
- Automated Assessment of Digital Images of Uterine Cervix Captured Using Transvaginal Device-A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Predictions of cervical cancer identification by photonic method combined with machine learning.Scientific reports · 2022Article
- Artificial Intelligence in Cervical Cancer Screening and Diagnosis.Frontiers in oncology · 2022Review
- Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT.Multimedia tools and applications · 2022Article
- Deep multiple-instance learning for abnormal cell detection in cervical histopathology images.Computers in biology and medicine · 2021Article
- Robust whole slide image analysis for cervical cancer screening using deep learning.Nature communications · 2021Article
- Single-cell conventional pap smear image classification using pre-trained deep neural network architectures.BMC biomedical engineering · 2021Article
- Artificial intelligence and computational pathology.Laboratory investigation; a journal of technical methods and pathology · 2021Review
- Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.JAMA network open · 2021Article
- Investigating Cervical Risk Factors that Lead to Cytological and Biopsy Examination.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2020Article
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.
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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.