%A REN Xue-ting, ZHAO Juan-juan, QIANG Yan, Saad Abdul RAUF, LIU Ji-hua %T Lung Cancer Subtype Recognition with Unsupervised Learning Combining Paired Learning and Image Clustering %0 Journal Article %D 2020 %J Computer Science %R 10.11896/jsjkx.190900073 %P 200-206 %V 47 %N 10 %U {https://www.jsjkx.com/CN/abstract/article_19414.shtml} %8 2020-10-15 %X 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.