Version 1
: Received: 9 July 2021 / Approved: 12 July 2021 / Online: 12 July 2021 (23:05:34 CEST)
How to cite:
LI, X.; Xu, Z.; Shen, X.; Zhou, Y.; Xiao, B.; Li, T. Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN. Preprints2021, 2021070277. https://doi.org/10.20944/preprints202107.0277.v1
LI, X.; Xu, Z.; Shen, X.; Zhou, Y.; Xiao, B.; Li, T. Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN. Preprints 2021, 2021070277. https://doi.org/10.20944/preprints202107.0277.v1
LI, X.; Xu, Z.; Shen, X.; Zhou, Y.; Xiao, B.; Li, T. Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN. Preprints2021, 2021070277. https://doi.org/10.20944/preprints202107.0277.v1
APA Style
LI, X., Xu, Z., Shen, X., Zhou, Y., Xiao, B., & Li, T. (2021). Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN. Preprints. https://doi.org/10.20944/preprints202107.0277.v1
Chicago/Turabian Style
LI, X., Binggang Xiao and Tie-Qiang Li. 2021 "Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN" Preprints. https://doi.org/10.20944/preprints202107.0277.v1
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 ar-chitecture for the detection of abnormal cervical cells in cytology images from 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 cer-vical 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 tra-ditional 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.
Keywords
Cervical cancer; Pap smear test; whole slide image (WSI); feature pyramid network (FPN); global context aware (GCA); region based convolutional neural networks (R-CNN); Region Proposal Network (RPN).
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
