Applicational progress and challenges of the artificial intelligence-aided cervical cancer cytological screening

doi:10.3877/cma.j.issn.2095-5820.2019.04.001

Abstract

Abstract:

Cervical cancer is one of the most common malignant tumors in women. Early detection and treatment are critical to reduce its mobility and motality. Cytological screening combined with HPV test is the best way for its early detection. However, the early diagnosis is impeded due to severely lack of cytopathologists. The application of artificial intelligency (AI) technology in cervical cancer screening will provide the best solution to enhance the screening efficiency and quality. We reviewed literatures of the AI-aided cervical cancer screening, described its progress of AI algorithm models, human screening and AI-aided screening interactive models in the cervical cytology; described the Prons and Cons of different machine and deep learning algorithms based on the bright and dark rules; analyzed available results of the AI-aided cervical cancer screening, and diacussed problems and challenges in exploring and applying of the AI-aided cervical cancer screening products. The purpose of this review is to provide insights for the research and development of the AI-aided cervical cancer screening to promote its application and implementation, which will contribute to reduce the mobility and motality of cervical cancer.

Key words: Cervical cancer, Cytological screening, Artificial intelligence, Deep learning, Machine learning

Shuanlong Che, Dong Liu, Si Liu, Pifu Luo. Applicational progress and challenges of the artificial intelligence-aided cervical cancer cytological screening[J]. Chinese Journal of Clinical Laboratory Management(Electronic Edition), 2019, 07(04): 193-198.

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References 28

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序号	作者	方法
1	Mariarputham和Stephen^[7]	提取细胞核和浆的24个特征参数，基于文本的宫颈细胞学分级系统设计算法。
2	Zhang等^[8]	双向融合关键特征的二叉树形网络法进行细胞核分割任务。
3	Komagata等^[9]	用细胞核的核面积、核周长等8个特征参数分析。
4	廖欣等^[10]	采用自适应双阈值法处理细胞图像，用改进Chan-Vase模型精确分割，结合病理知识图谱，构建相应的正交特征集。

序号	作者	方法
1	Mariarputham和Stephen^[7]	提取细胞核和浆的24个特征参数，基于文本的宫颈细胞学分级系统设计算法。
2	Zhang等^[8]	双向融合关键特征的二叉树形网络法进行细胞核分割任务。
3	Komagata等^[9]	用细胞核的核面积、核周长等8个特征参数分析。
4	廖欣等^[10]	采用自适应双阈值法处理细胞图像，用改进Chan-Vase模型精确分割，结合病理知识图谱，构建相应的正交特征集。

序号	作者	方法
1	Song等^[11]	基于超像素和卷积神经网络方法分割细胞和细胞核。
2	Lu等^[12]	利用多层级函数联合法优化分割重叠细胞。
3	Zhao等^[13]	将块图像分为六大类。进行块图像采集，去除背景块，三色模型特征与参数提取，用SVM分类与性能评估。
4	胡卉和蔡金清^[14]	基于深度卷积神经网络Res Net 50迁移的学习方法，对细胞图像数据集进行训练与特征提取。
5	郑欣等^[15]	采用Res Net 50模型的YOLO v2网络基础特征进行模块提取，病理医生对细胞簇标注。

序号	作者	方法
1	Song等^[11]	基于超像素和卷积神经网络方法分割细胞和细胞核。
2	Lu等^[12]	利用多层级函数联合法优化分割重叠细胞。
3	Zhao等^[13]	将块图像分为六大类。进行块图像采集，去除背景块，三色模型特征与参数提取，用SVM分类与性能评估。
4	胡卉和蔡金清^[14]	基于深度卷积神经网络Res Net 50迁移的学习方法，对细胞图像数据集进行训练与特征提取。
5	郑欣等^[15]	采用Res Net 50模型的YOLO v2网络基础特征进行模块提取，病理医生对细胞簇标注。

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