基于弱监督ECOC算法的肺结节辅助检测
Pulmonary Nodule Aided Detection Based on Weakly Supervised

肺结节的准确分类与识别是计算机辅助诊断系统在肺癌诊断领域应用的关键，同时也面临巨大的挑战。该技术不仅在特征表示、样本标记等方面存在发展的瓶颈，而且目前缺少准确、有效的分类识别算法。本文提出了一种结合弱监督纠错输出编码（Error correcting output codes, ECOC）算法和肺结节形状特征表达的肺结节多分类算法。为了提高分类识别的准确率，本文对肺结节的形状特征进行了详细的分析，并提出了一系列准确的形状特征描述向量。在分类识别阶段，算法训练学习了利用专家对肺结节标记信息标记的少量样本，并生成二类分类器，获得编码矩阵。最后，通过计算测试样本编码和编码矩阵每一行的汉明距离，确定样本所属类别。实验结果表明，本文方法能够获得更加准确的分类结果。
Accurate classification and recognition of pulmonary nodules are key process of lung cancer computer aided diagnosis (CAD) system. Meanwhile，there are still some scientific and technical challenges, including the difficulty of the feature representation and samples labeled, and the lack of accurate and effective recognition and classification algorithms. A multi classification algorithm is presented combining weakly supervised ECOC algorithm with pulmonary nodules features expression of shape. In order to improve the classification accuracy, we select a series of accurate shape feature description vectors by deliberating the shape features of pulmonary nodules. During the training phase, the coded matrix is constructed by a series of binary classifiers, which are generated by a small amount of labeled pulmonary nodules from experts. Finally, the Humming distance between the code of testing sample and each row of the coded matrix are calculated to determine the category of the testing sample. Experimental results show that the proposed method can obtain more accurate classification results.