关键词: 注意力, 多维度, 多特征, 特征融合, 浸润性腺癌诊断

Abstract:

Nowadays, lung cancer is one of cancers that endanger human health and have a high fatality rate. Annual CT screening can detect curable pulmonary nodules. At present, it mainly depends on the experience of radiologists to diagnose pulmonary nodules. The adenocarcinoma nodules which have the worst infiltrating should be diagnosed by the method of intraoperative rapid freezing, and the accuracy for small diameter nodules is low. For above problems, this paper proposes an algorithm for the diagnosis of adenocarcinoma nodules in small glass-ground lung nodules based on CT images. Two different dimensions of data are generated for extracting spatial and plane characteristics, and two different dimensions of networks are constructed by the residual learning unit combined with the attention mechanism. By combining the feature vectors extracted by two networks, the diagnosis of adenocarcinoma nodules can be concluded. This algorithm is experimented in the dataset collected from Shanghai Chest Hospital. It consists of 1,760 small glass-ground lung nodules including 340 adenocarcinoma nodules and 1,420 other nodules. The cross validation is performed in this experiment, the classification accuracy of this algorithm reaches 82.7%, the sensitivity reaches 82.9% and the specificity reaches 82.6%.

Key words: attention, multi-dimensional, multi-features, feature fusion, diagnosis of adenocarcinoma