ArticleIEEE transactions on medical imaging2015
Multimodal entity coreference for cervical dysplasia diagnosis.
Article in IEEE transactions on medical imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 83 citations in OpenAlex.
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- HMCFormer (hierarchical multi-scale convolutional transformer): a hybrid CNN+Transformer network for intelligent VIA screening.PeerJ. Computer science · 2025Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- Transferable and Interpretable Treatment Effectiveness Prediction for Ovarian Cancer via Multimodal Deep Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis.Journal of gynecologic oncology · 2022Article
- Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet.Journal of healthcare engineering · 2022Article
- Computer-aided diagnosis of cervical dysplasia using colposcopic images.Frontiers in oncology · 2022Article
- Artificial Intelligence in Cervical Cancer Screening and Diagnosis.Frontiers in oncology · 2022Review
- DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence.BMC medicine · 2020Article
- Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.Journal of digital imaging · 2020Article
- A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.International journal of molecular sciences · 2019Review
- An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.Journal of the National Cancer Institute · 2019Observational
- Automatic Approach for Cervical Cancer Detection and Segmentation Using Neural Network ClassifierAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS ClassifierAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings.Journal of digital imaging · 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
- Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation.Pattern recognition · 2017Article
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
Cervical cancer is the second most common type of cancer for women. Existing screening programs for cervical cancer, such as Pap Smear, suffer from low sensitivity. Thus, many patients who are ill are not detected in the screening process. Using images of the cervix as an aid in cervical cancer screening has the potential to greatly improve sensitivity, and can be especially useful in resource-poor regions of the world. In this paper, we develop a data-driven computer algorithm for interpreting cervical images based on color and texture. We are able to obtain 74% sensitivity and 90% specificity when differentiating high-grade cervical lesions from low-grade lesions and normal tissue. On the same dataset, using Pap tests alone yields a sensitivity of 37% and specificity of 96%, and using HPV test alone gives a 57% sensitivity and 93% specificity. Furthermore, we develop a comprehensive algorithmic framework based on Multimodal Entity Coreference for combining various tests to perform disease classification and diagnosis. When integrating multiple tests, we adopt information gain and gradient-based approaches for learning the relative weights of different tests. In our evaluation, we present a novel algorithm that integrates cervical images, Pap, HPV, and patient age, which yields 83.21% sensitivity and 94.79% specificity, a statistically significant improvement over using any single source of information alone.
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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.