Abnormal region detection in cervical smear images based on fully convolutional network

Zhang, Jianwei; He, Junting; Chen, Tianfu; Liu, Zhen-Mei; Chen, Danni

doi:10.1049/iet-ipr.2018.6032

Cited by 13 publications

(13 citation statements)

References 37 publications

(41 reference statements)

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“…The most important findings of our study are as follows: (1). There are currently few network architectures designed specifically for cervical cancer cell detection in Pap smear images [42,[44][45][46]. Based on one of the currently top-performing detectors faster RCCN-FPN [57]…”

Section: The Main Findings Of the Studymentioning

confidence: 99%

“…Xu et al [45] used the generic Faster RCNN for the detection of abnormal cells in cervical smear images scanned at 20× and showed that detection of various abnormal cells was feasible. Zhang et al [46] tested a R-FCN model for cervical cancer screening of liquid based cytology (LBC) images. Their performance evaluation was based on an interesting concept called hit degree which ignores the precise IOU threshold, and a hit recall was counted if the ground truth boxes hit by any detection result box.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN

Li¹,

Xu²,

Shen³

et al. 2021

Preprint

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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.

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Section: The Main Findings Of the Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN

Li¹,

Xu²,

Shen³

et al. 2021

Preprint

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show abstract

“…Their conclusion proves that the accurate detection and classification of various abnormal cells and the statistics of various abnormal cells through computer technology can draw a more comprehensive conclusion, thus illustrating the object detection technology can be effectively applied to the diagnosis of cervical cancer. A novel abnormal region detection method for cervical screening in LBC images based on the R‐FCN [42] was proposed in [35]. It also defined a new measurement method hit degree to describe the degree to which each detection area matches the corresponding ground truth.…”

Section: Related Workmentioning

confidence: 99%

“…With the development of artificial intelligence and deep learning technology, some methods begin to consider the use of deep learning methods for cervical cancer detection [34–36]. Due to the advantages of CNNs in feature extraction, these methods based on deep learning show better performance than traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

Liu

et al. 2020

IET image process

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With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid‐based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial‐aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.

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“…Wu et al [ 14 ] investigated the potential for using Principal Component Analysis (PVA) and Adaptive Median Filter (AMF) to improve four algorithms, including R-FCN and YOLOv3. Zhang et al [ 15 ] proposed a novel abnormal region detection approach for cervical screening based on R-FCN. Morrell et al [ 16 ] presented a neural net architecture based on R-FCN to suit mammograms.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN

Jiang

Zeng

Xiao

et al. 2020

Computational and Mathematical Methods in Medicine

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An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the “You Only Look Once” version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.

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Abnormal region detection in cervical smear images based on fully convolutional network

Cited by 13 publications

References 37 publications

Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN

Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN

Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN

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