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Automatic model for cervical cancer screening based on convolutional neural network: a retrospective multicohort multicenter study

机译:基于卷积神经网络的宫颈癌筛查自动模型:回顾性多中心研究

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摘要

Framework of the proposed convolutional neural network (CNN) system for cervical cancer screening. The convolution network extracts the image information to obtain the feature map, and the proposal network screens the target region to obtain the target position information on the basis of the feature map. The pathological cells and their locations were identified by a convolutional classifier based on the feature map and target location. Pathological cell information was obtained by combining the recognition results of the two networks. The results of the two models are similar. However, the two structural models are trained with images of different magnification levels of 200X and 400X, and the parameters of the two models are different
机译:宫颈癌筛查拟议卷积神经网络(CNN)系统的框架。卷积网络提取图像信息以获得特征映射,并且提议网络筛选目标区域基于特征图获得目标位置信息。基于特征图和目标位置,通过卷积分类器识别病理细胞及其位置。通过组合两个网络的识别结果来获得病理细胞信息。两种模型的结果是相似的。但是,这两个结构模型受到200倍和400倍的不同放大率的图像培训,并且两种模型的参数是不同的

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