ArticleDiagnostics (Basel, Switzerland)2022
Radiomics Diagnostic Tool Based on Deep Learning for Colposcopy Image Classification.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs.Scientific reports · 2026Article
- Radiomic Analysis as a Powerful Tool for Cytological Images of Benign Thyroid Nodules Treated by Thermal Radiofrequency Ablation.Bioengineering (Basel, Switzerland) · 2026Article
- Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.Scientific reports · 2025Article
- Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders.Scientific reports · 2024Article
- BraNet: a mobil application for breast image classification based on deep learning algorithms.Medical & biological engineering & computing · 2024Article
- Fusion of shallow and deep features fromQuantitative imaging in medicine and surgery · 2024Article
- CervicoXNet: an automated cervicogram interpretation network.Medical & biological engineering & computing · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 4 countries.
Funding
Abstract
backgroundColposcopy imaging is widely used to diagnose, treat and follow-up on premalignant and malignant lesions in the vulva, vagina, and cervix. Thus, deep learning algorithms are being used widely in cervical cancer diagnosis tools. In this study, we developed and preliminarily validated a model based on the Unet network plus SVM to classify cervical lesions on colposcopy images.
Indexed as
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What OpenQuestion holds
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.