Cervical Cell Image Classification Based On Multiple Attention Fusion

Su, Xin; Shi, Jun; Peng, Yusheng; Zheng, Liping

doi:10.1109/cisp-bmei53629.2021.9624420

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Many studies have started to focus on improving neural network performance. For example, to improve the classification performance of cervical cell images, in [ 12 ], a squeeze-and-excitation block and a spatial attention block were introduced to fuse the channel and spatial features. In [ 13 ], ResNet50 was selected as the backbone network to which a feature pyramid pooling layer and a long short-term memory module (LSTM) were added and its effectiveness was experimentally demonstrated.…”

Section: Related Workmentioning

confidence: 99%

Cervical Cell Image Classification-Based Knowledge Distillation

Zhang

et al. 2022

Biomimetics

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Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset.

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Section: Related Workmentioning

confidence: 99%

Cervical Cell Image Classification-Based Knowledge Distillation

Zhang

et al. 2022

Biomimetics

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A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis

Jiang,

Li,

Shen

et al. 2023

Artif Intell Rev

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 80 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future research in automated cervical cytology screening.

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Improved White Blood Cell Image Classification Using Deep Convolutional Neural Networks with Attention Mechanism

Qian

2023

2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)

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Cervical Cell Image Classification Based On Multiple Attention Fusion

Cited by 3 publications

References 8 publications

Cervical Cell Image Classification-Based Knowledge Distillation

Cervical Cell Image Classification-Based Knowledge Distillation

A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis

Improved White Blood Cell Image Classification Using Deep Convolutional Neural Networks with Attention Mechanism

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