Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis rate. Existing computer-assisted algorithms for analyzing cervigram images have neglected that colposcopy is a sequential and multistate process, which is unsuitable for clinical applications. In this work, we construct a cervigram-based recurrent convolutional neural network (C-RCNN) to classify different CIN grades and cervical cancer. Convolutional neural networks are leveraged to extract spatial features. We develop a sequence-encoding module to encode discriminative temporal features and a multistate-aware convolutional layer to integrate features from different states of cervigram images. To train and evaluate the performance of C-RCNN, we leveraged a dataset of 4,753 real cervigrams and obtained 96.13% test accuracy with a specificity and sensitivity of 98.22% and 95.09%, respectively. Areas under each receiver operating characteristic curves are above 0.94, proving that visual representations and sequential dynamics can be jointly and effectively optimized in the training phase. Comparative analysis demonstrated the effectiveness of the proposed C-RCNN against competing methods, showing significant improvement over only focusing on a single frame. This architecture can be extended to other applications in medical image analysis

中文翻译：

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使用具有多状态CNN功能的LSTM自动对连续子宫颈图像进行CIN分级预测

宫颈癌是全世界女性中第二大最常见的癌症。在临床实践中，阴道镜检查是筛查宫颈上皮内瘤样病变（CIN）和宫颈癌的必不可少的部分，但其误诊率很高。现有的用于分析宫颈图像的计算机辅助算法已经忽略了阴道镜检查是一个连续的多状态过程，不适合临床应用。在这项工作中，我们构建了一个基于子宫颈图的循环卷积神经网络（C-RCNN），以对不同的CIN等级和宫颈癌进行分类。卷积神经网络被利用来提取空间特征。我们开发了一个序列编码模块来编码判别性时间特征和一个多状态感知卷积层，以整合来自宫颈图像不同状态的特征。为了训练和评估C-RCNN的性能，我们利用了4,753个真实子图的数据集，获得了96.13％的测试准确度，特异性和灵敏度分别为98.22％和95.09％。每个接收器工作特性曲线下的面积均大于0.94，证明了在训练阶段可以共同有效地优化视觉表示和顺序动力学。比较分析证明了所提出的C-RCNN相对于竞争方法的有效性，与仅关注单个框架相比，显示出显着的改进。该体系结构可以扩展到医学图像分析中的其他应用程序 每个接收器工作特性曲线下的面积均大于0.94，证明了在训练阶段可以共同有效地优化视觉表示和顺序动力学。比较分析证明了所提出的C-RCNN相对于竞争方法的有效性，与仅关注单个框架相比，显示出显着的改进。该体系结构可以扩展到医学图像分析中的其他应用程序 每个接收器工作特性曲线下的面积均大于0.94，证明了在训练阶段可以共同有效地优化视觉表示和顺序动力学。比较分析证明了所提出的C-RCNN相对于竞争方法的有效性，与仅关注单个框架相比，显示出显着的改进。该体系结构可以扩展到医学图像分析中的其他应用程序