Zhou Zhi Hua, Jiang Yuan, Yang Yu Bin, Chen Shi Fu
National Laboratory for Novel Software Technology, Nanjing University, 210093, Nanjing, PR China.
Artif Intell Med. 2002 Jan;24(1):25-36. doi: 10.1016/s0933-3657(01)00094-x.
An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.
人工神经网络集成是一种学习范式，其中多个人工神经网络被联合用于解决一个问题。本文提出了一种名为基于神经网络集成检测（NED）的自动病理诊断程序，该程序利用人工神经网络集成来识别从待诊断对象身体获取的针吸活检标本图像中的肺癌细胞。该集成基于两级集成架构构建。第一级集成用于以高置信度判断一个细胞是否正常，其中每个单独的网络分别只有两个输出，即正常细胞或癌细胞。这些单独网络的预测通过本文提出的一种新方法进行组合，即全票表决，只有当所有单独网络都判断一个细胞正常时，才将其判断为正常细胞。第二级集成用于处理被第一级集成判断为癌细胞的细胞，其中每个单独的网络分别有五个输出，即腺癌、鳞状细胞癌、小细胞癌、大细胞癌和正常细胞，前四种是不同类型的肺癌细胞。这些单独网络的预测通过一种常用方法进行组合，即多数表决。通过采用这些技术，NED不仅实现了高总体识别率，而且实现了低假阴性识别率，即低将癌细胞判断为正常细胞的比率，这对于减少癌症患者漏诊从而挽救生命非常重要。
