【摘要】 目的基于人工智能技术的前列腺癌诊断是目前的研究热点。然而现有的智能诊断方法大多只能利用MRI、CT等图片数据进行前列腺癌诊断,还无法对前列腺癌的图数据进行处理,局限性很大,诊断性能还有待提高。为了弥补这一不足,提出一种基于图卷积神经网络(GCN)的前列腺癌诊断模型(PCa-GCN)。方法首先从各大医院收集到前列腺癌检查数据样本,然后基于jieba分词、词袋模型、最大熵模型等预处理来构建得到病历图。接着将病历图作为GCN的输入来学习得到前列腺癌特征的图嵌入表示,最后采用基于Sigmoid的逻辑回归实现特征与前列腺癌之间的映射,完成对前列腺癌的准确诊断。结果基于K折交叉验证的实验结果表明,PCa-GCN模型在召回率和ROC曲线方面的性能优于其他诊断方法。结论 PCa-GCN模型可实现前列腺癌的精准诊断,为前列腺癌数据分析、疾病预防提供技术支持。更多还原

【Abstract】 Objective Diagnosis of prostate cancer based on artificial intelligence technology attracts great academic attention at present. However, most of the existing intelligent diagnostic methods can only collect MRI, CT and other image data for prostate cancer diagnosis, and cannot process such data, resulting in huge limitations and unsatisfactory performance. In order to solve the problem, a prostate cancer diagnosis model based on graph convolutional neural network(PCa-GCN) is proposed in this paper. Methods Firstly, prostate cancer data samples were collected from various hospitals, and then the graph of medical record was constructed based on the preprocessing with the jieba word segmentation, the bag-of-words model and the maximum entropy model. Then the graph of medical record was inputted for GCN to learn the graph embedding representation of characteristics of prostate cancer. Finally, the mapping between such features and prostate cancer was accomplished by logic regression based on sigmoid for accurate diagnosis of prostate cancer. Results Experimental results based on k-fold cross validation showed that the PCa-GCN model is superior to the other diagnostic methods in terms of recall rate and ROC curve. Conclusion PCa-GCN model achieved precise diagnosis of prostate cancer and can provide technical support for prostate cancer data analysis and disease prevention.更多还原