ArticleCurrent oncology (Toronto, Ont.)2021
Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN.
Article in Current oncology (Toronto, Ont.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 72 citations in OpenAlex.
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- Multi-Modal Graph Neural Networks for Colposcopy Data Classification and Visualization.Cancers · 2025Article
- Multiscale deformed attention networks for white blood cell detection.Scientific reports · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Automated analysis of digital medical images in cervical cancer screening: A systematic review.medRxiv : the preprint server for health sciences · 2024Article
- Pixel-wise segmentation of cells in digitized Pap smear images.Scientific data · 2024Article
- Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.Interdisciplinary sciences, computational life sciences · 2024Article
- Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.Frontiers in oncology · 2024Article
- CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer.Heliyon · 2023Article
- Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review.Journal of digital imaging · 2023Review
- Prevention Strategies and Early Diagnosis of Cervical Cancer: Current State and Prospects.Diagnostics (Basel, Switzerland) · 2023Review
- YOLOv7-RAR for Urban Vehicle Detection.Sensors (Basel, Switzerland) · 2023Article
- Adaptive visual detection of industrial product defects.PeerJ. Computer science · 2023Article
- Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.Bio-design and manufacturing · 2023Review
- Article
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence.Current oncology (Toronto, Ont.) · 2022Article
- BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.Bioengineering (Basel, Switzerland) · 2022Article
- The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cervical image dataset of "Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using traditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.
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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.