Complete three‐phase detection framework for identifying abnormal cervical cells

Zhao, Lili; Li, Kuan; Yin, Jianping; Liu, Qiang; Wang, Siqi

doi:10.1049/iet-ipr.2016.0788

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…Most previous studies on cervical cytology using DL technology aimed to classify or detect atypical epithelial cells at a single-cell level, where many single cells in the image were classified or detected one by one under high magnification [1][2][3][4][5][6][7][8]11]. However, depending on the WSI scanner model and the imaging range, a WSI will generate approximately 900 tiled images at 10× and 14,000 at 40×.…”

Section: Discussionmentioning

confidence: 99%

“…For example, cytology is a less invasive procedure than histopathology for collecting cells from the lesions of patients directly. Recently, machine learning or DL technologies used for diagnosing smears and liquid-based cytology (LBC) specimens obtained from the cervix have been investigated utilizing digitized glass slide images known as whole-slide images (WSIs) [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the data for specific purposes must be collected manually, which is time-consuming. In addition, previous studies on cytological evaluations using DL methods were typically meant to classify and detect atypical epithelial cells at a higher magnification, such as 20× or 40× [1][2][3][4][5][6][7][8]11], resulting in the burden of analyzing as many as 2,000 to 5,000 single cells present in a specimen. A systematic review also revealed that most studies on cytological diagnosis using DL methods had been conducted in experimental settings and have not yet been implemented in clinical practice [15].…”

Section: Introductionmentioning

confidence: 99%

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Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

et al. 2023

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Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

et al. 2023

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“…Genctav et al and Nayar and Wilbur grouped and sorted according to TBS (cervical cell standard classification system) [16,17] and used the hierarchical clustering algorithm to construct decision trees for classification. Zhao et al used a three-stage strategy to classify cervical cancerous cells [18], extracted 120 Witt's sign, and used it for the training of linear classifiers.…”

Section: Related Workmentioning

confidence: 99%

Efficient Cervical Cell Lesion Recognition Method Based on Dual Path Network

Qin

Gao

Sun

et al. 2022

Wireless Communications and Mobile Computing

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Ubiquitous computing and artificial intelligence are widely used in the field of Wise Information Technology of Med. It promotes the development of intelligent diagnosis and treatment, as well as reducing the workload of doctors’ diagnosis and promoting the improvement of the diagnosis level of medical institutions in remote areas. The accurate detection of cervical cytopathy is related to the precise treatment and rehabilitation of patients. However, the low rate of accuracy in the existing cervical cytopathy image detection cannot satisfy the application needs of intelligent diagnosis. In this paper, a dual path network efficient detection method for cervical cytopathy (the proposed DSRNet50) is proposed, which is based on the deep learning method, and combines residual structure and dense connection. The proposed DSRNet50 is mainly based on residual structure, supplemented by dense connection paths, which improves the utilization of features, and maintains the ability of exploring new features by reducing feature redundancy. Meanwhile, the proposed DSRNet50 leverages packet convolution to reduce the computation burden of the network and eliminate the over fitting phenomenon of the network. The proposed DSRNet50 further exploits channel attention mechanism to recalibrate important features to suppress the propagation of irrelevant information. We use the Herlev dataset to verify the proposed DSRNet50 in the terms of the detection accuracy, parameter quantity, and computing complexity. The experiment results are presented to show the achievable performance of the proposed DSRNet50.

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“…In general, researchers prefer composite classification method supported by various extracted features rather than a single one. In [20], a two‐category classification support vector machine is built to do classification via 160‐dimensional features including cell features and nucleus features. A quantum hybrid intelligent approach that blends the adaptive search capability of the quantum‐behaved particle swarm optimisation method is performed to select the best subset features, used by fuzzy k ‐nearest neighbours algorithm to classify normal cells and abnormal cells [21].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Chen

et al. 2019

IET Image Processing

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Complete three‐phase detection framework for identifying abnormal cervical cells

Cited by 11 publications

References 29 publications

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Efficient Cervical Cell Lesion Recognition Method Based on Dual Path Network

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

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