计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 312-317.doi: 10.11896/j.issn.1002-137X.2017.08.054
所属专题： 医学图像
赵鑫,强彦,葛磊
ZHAO Xin, QIANG Yan and GE Lei
摘要： 近年来,深度学习技术在肺癌诊断方面得到了广泛的应用,但现有的研究主要集中于肺部CT图像。为了有效提高肺结节的诊断性能,提出一种基于双模态深度降噪自编码的肺结节诊断方法。首先,分别从肺部CT和PET图像中得到肺结节区域的特征信息；然后,以候选结节的PET/CT图像作为整个深度自编码网络的输入,并对高层信息进行学习；最后,采用融合策略对多种特征进行融合并将其作为整个框架的输出。实验结果表明,提出的方法可以达到92.81%的准确率、91.75%的敏感度和1.58%的特异性,且优于其他方法的诊断性能,更适用于肺结节良/恶性的辅助诊断。
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