ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
A fusion-based approach for uterine cervical cancer histology image classification.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 43 citations in OpenAlex.
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma.Scientific reports · 2024Article
- Deep multiple-instance learning for abnormal cell detection in cervical histopathology images.Computers in biology and medicine · 2021Article
- A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability.Medical & biological engineering & computing · 2021Article
- EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.Journal of pathology informatics · 2020Article
- Skin Disease Recognition Method Based on Image Color and Texture Features.Computational and mathematical methods in medicine · 2018Article
- Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.Journal of pathology informatics · 2018Article
- Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade.Journal of healthcare engineering · 2018Article
- Overexpression of ezrin and galectin-3 as predictors of poor prognosis of cervical cancer.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2017Article
- Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification.IEEE journal of biomedical and health informatics · 2016Article
- Enhancements in localized classification for uterine cervical cancer digital histology image assessment.Journal of pathology informatics · 2016Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Expert pathologists commonly perform visual interpretation of histology slides for cervix tissue abnormality diagnosis. We investigated an automated, localized, fusion-based approach for cervix histology image analysis for squamous epithelium classification into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The epithelium image analysis approach includes medial axis determination, vertical segment partitioning as medial axis orthogonal cuts, individual vertical segment feature extraction and classification, and image-based classification using a voting scheme fusing the vertical segment CIN grades. Results using 61 images showed at least 15.5% CIN exact grade classification improvement using the localized vertical segment fusion versus global image features.
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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.