联合成对学习和图像聚类的无监督肺癌亚型识别

Lung Cancer Subtype Recognition with Unsupervised Learning Combining Paired Learning and Image Clustering

基因诊断是近年来提高肺癌治愈率的一种新型且有效的方法,但这种方法存在基因检测时间长、费用高、侵入式取样损伤大的问题。文中提出了基于成对学习和图像聚类的无监督学习的肺癌亚型识别方法。首先,采用无监督卷积特征融合网络用于学习肺癌CT图像的深度表示,有效地捕捉被忽略的重要特征信息,并使用包含不同层次抽象信息的最终融合特征来表征肺癌亚型。然后,使用联合成对学习和图像聚类的分类学习框架进行建模,充分利用学习到的特征表示,确保有效的聚类学习,以取得更高的分类精度。最后,利用生存分析和基因分析对肺癌亚型进行多角度验证。在合作医院和TCGA-LUAD数据集上的实验结果表明,该方法通过可靠无创的影像分析和放射成像技术,发现了3种具有不同分子特征的肺癌影像亚型,在降低基因检测问题的同时可有效辅助医师进行精准诊断和个性化治疗,进而提高肺癌患者的治愈生存率。

In recent years,gene diagnosis has been one of the new and effective methods to improve the cure rate of lung cancer,but it has the problems of time-consuming,high cost and serious damage from invasive sampling.In this paper,an unsupervised learning method of Lung cancer subtype recognition based on paired learning and image clustering is proposed.Firstly,the unsupervised convolution feature fusion network is used to learn the deep representation of lung cancer CT images and effectively capture the important feature information that is ignored,and the final fusion features containing different levels of abstract information is used to represent lung cancer subtypes.Then,the classification learning framework of combined paired learning and image clustering is used for modeling,and the learnt feature representation is fully utilized to ensure effective clustering learning,so as to achieve higher classification accuracy.Finally,survival analysis and gene analysis are used to verify lung cancer subtypes from multiple perspectives.Experiments on the data sets of the cooperative hospital and TCGA-LUAD show that,through reliable and non-invasive image analysis and radiological imaging technology,three subtypes of lung cancer with different molecular characte-ristics have been found by this method.It can effectively assist doctors in accurate diagnosis and personalized treatment while reducing problems in gene detection,so as to improve the survival rate of lung cancer patients.