Identification of abnormal cervical cells is a significant problem in computer-aided diagnosis of cervical cancer. In this study, we develop an artificial intelligence (AI) system, named CytoBrain, to automatically screen abnormal cervical cells to help facilitate the subsequent clinical diagnosis of the subjects. The system consists of three main modules: 1) the cervical cell segmentation module which is responsible for efficiently extracting cell images in a whole slide image (WSI); 2) the cell classification module based on a compact visual geometry group (VGG) network called CompactVGG which is the key part of the system and is used for building the cell classiffier; 3) the visualized human-aided diagnosis module which can automatically diagnose a WSI based on the classification results of cells in it, and provide two visual display modes for users to review and modify. For model construction and validation, we have developed a dataset containing 198 952 cervical cell images (60 238 positive, 25 001 negative, and 113 713 junk) from samples of 2 312 adult women. Since CompactVGG is the key part of CytoBrain, we conduct comparison experiments to evaluate its time and classification performance on our developed dataset and two public datasets separately. The comparison results with VGG11, the most efficient one in the family of VGG networks, show that CompactVGG takes less time for either model training or sample testing. Compared with three sophisticated deep learning models, CompactVGG consistently achieves the best classification performance. The results illustrate that the system based on CompactVGG is efficient and effective and can support for large-scale cervical cancer screening.

在宫颈癌的计算机辅助诊断中，异常子宫颈细胞的识别是一个重要的问题。在这项研究中，我们开发了一个名为CytoBrain的人工智能（AI）系统，可以自动筛选异常子宫颈细胞，以帮助促进受试者的后续临床诊断。该系统由三个主要模块组成：1）宫颈细胞分割模块，负责有效地提取整个玻片图像（WSI）中的细胞图像；2）基于称为CompactVGG的紧凑视觉几何组（VGG）网络的单元分类模块，它是系统的关键部分，用于建立单元分类。3）可视化的人工诊断模块，可以根据其中的细胞分类结果自动诊断WSI，并提供两种视觉显示模式供用户查看和修改。对于模型构建和验证，我们已经开发了一个数据集，其中包含来自2 312名成年女性的样本的198 952例宫颈细胞图像（60 238例阳性，25 001例阴性和113 713例垃圾）。由于CompactVGG是CytoBrain的关键部分，我们进行比较实验以分别在我们开发的数据集和两个公共数据集上评估其时间和分类性能。与VGG网络家族中效率最高的VGG11的比较结果表明，CompactVGG花费较少的时间进行模型训练或样品测试。与三种复杂的深度学习模型相比，CompactVGG始终获得最佳的分类性能。