ArticleIEEE journal of biomedical and health informatics2016
Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification.
Article in IEEE journal of biomedical and health informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 93 citations in OpenAlex.
- Patch-to-slide fusion deep learning model for histological diagnosis of early pregnancy loss including hydatidiform mole.NPJ digital medicine · 2026Article
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- A stochastic structural similarity guided approach for multi-modal medical image fusion.Scientific reports · 2025Article
- Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma.Scientific reports · 2024Article
- Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.European radiology · 2024Article
- ASATrans: Adaptive spatial aggregation transformer for cervical nuclei segmentation on rough edges.PloS one · 2024Article
- Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- Multimodal brain image fusion based on error texture elimination and salient feature detection.Frontiers in neuroscience · 2023Article
- Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques.Medical devices (Auckland, N.Z.) · 2022Article
- A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability.Medical & biological engineering & computing · 2021Article
- Selective synthetic augmentation with HistoGAN for improved histopathology image classification.Medical image analysis · 2021Article
- Evaluation Technology of Classroom Students' Learning State Based on Deep Learning.Computational intelligence and neuroscience · 2021Article
- AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features.Sensors (Basel, Switzerland) · 2020Article
- EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.Journal of pathology informatics · 2020Article
- DeepCIN: Attention-Based Cervical histology Image Classification with Sequential Feature Modeling for Pathologist-Level Accuracy.Journal of pathology informatics · 2020Article
- 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
- Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.Journal of pathology informatics · 2018Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Routinely, expert pathologists visually examine histology slides for cervix tissue abnormality assessment. In previous research, we investigated an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 61 digitized histology images. This paper introduces novel acellular and atypical cell concentration features computed from vertical segment partitions of the epithelium region within digitized histology images to quantize the relative increase in nuclei numbers as the CIN grade increases. Based on the CIN grade assessments from two expert pathologists, image-based epithelium classification is investigated with voting fusion of vertical segments using support vector machine and linear discriminant analysis approaches. Leave-one-out is used for the training and testing for CIN classification, achieving an exact grade labeling accuracy as high as 88.5%.
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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.