Computer Science ›› 2020, Vol. 47 ›› Issue (10): 200-206.doi: 10.11896/jsjkx.190900073

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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

REN Xue-ting1, ZHAO Juan-juan1, QIANG Yan1, Saad Abdul RAUF1, LIU Ji-hua2   

  1. 1 School of Information and Computer Science,Taiyuan University of Technology,Taiyuan 030024,China
    2 School of Computer Science and Technology,Lvliang University,Lvliang,Shanxi 033000,China
  • Received:2019-09-10 Revised:2019-11-15 Online:2020-10-15 Published:2020-10-16
  • About author:EN Xue-ting,born in 1994,postgra-duate,is a member of China Computer Federation.Her main research interests include medical image processing and deep learning.
    ZHAO Juan-juan,born in 1975,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include intelligent information processing and image recognition.
  • Supported by:
    National Natural Science Foundation of China (61872261),Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (VRLAB2018A08) and International Science and Technology Cooperation Project of Shanxi Key R&D Program (201803D421036)

Abstract: 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.

Key words: Deep representation, Image clustering, Lung cancer subtype recognition, Paired learning, Unsupervised learning

CLC Number: 

  • TP391.41
[1]SHAO L,SONG Z B,ZHANG Y P,et al.Advances of Molecular Subtype and Targeted Therapy of Lung Cancer[J].Chinese Journal of Lung Cancer,2012,15(9):545-552.
[2]YOUNG J D,CAI C,LU X.Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma[J].BMC Bioinformatics,2017,18(S11):381.
[3]MAZUROWSKI M A,ZHANG J,GRIMM L J,et al.Radio-genomic Analysis of Breast Cancer:Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging[J].Radiology,2014,273(2):365-372.
[4]WILSON R,DEVARAJ A.Radiomics of pulmonary nodules and lung cancer[J].Translational Lung Cancer Research,2017,6(1):86.
[5]ZHANG Y,OIKONOMOU A,WONG A,et al.Radiomics-basedprognosis analysis for non-small cell lung cancer[J].Scientific Reports,2017,7:46349.
[6]THAWANI R,MCLANE M,BEIG N,et al.Radiomics and radiogenomics in lung cancer:a review for the clinician[J].Lung Cancer,2018,115:34-41.
[7]WU J,CUI Y,SUN X L,et al.Unsupervised Clustering ofQuantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways[J].Clinical Cancer Research,2017,23(13):3334-3342.
[8]ITAKURA H,ACHROL A S,MITCHELL L A,et al.Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities[J].Science Translational Medicine,2015,7(303):303ra138-303ra138.
[9]YANG J,PARIKH D,BATRA D.Joint unsupervised learning of deep representations and image clusters [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:5147-5156.
[10]HAO R,QIANG Y,LIAO X,et al.An automatic detectionmethod for lung nodules based on multi-scale enhancement filters and 3D shape features[J].KSII Transactions on Internet & Information Systems,2019,13(1).
[11]WEST L,VIDWANS S J,CAMPBELL N P,et al.A novel classification of lung cancer into molecular subtypes[J].PloS one,2012,7(2):e31906.
[12]WU M,MA J.Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer[J].Academic Radiology,2016,24(4):426-434.
[13]RATHORE S,AKBARI H,ROZYCKI M,et al.Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics,offering prognostic value beyond IDH1[J].Scientific Reports,2018,8(1):5087-5098.
[14]LAMBIN P,RIOS-VELAZQUEZ E,LEIJENAAR R,et al.Radiomics:extracting more information from medical images using advanced feature analysis[J].European Journal of Cancer,2012,48(4):441-446.
[1] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[3] HOU Hong-xu, SUN Shuo, WU Nier. Survey of Mongolian-Chinese Neural Machine Translation [J]. Computer Science, 2022, 49(1): 31-40.
[4] CHEN Yang, WANG Jin-liang, XIA Wei, YANG Hao, ZHU Run, XI Xue-feng. Footprint Image Clustering Method Based on Automatic Feature Extraction [J]. Computer Science, 2021, 48(6A): 255-259.
[5] LI Xiang-li, JIA Meng-xue. Nonnegative Matrix Factorization Algorithm with Hypergraph Based on Per-treatments [J]. Computer Science, 2020, 47(7): 71-77.
[6] LI Jin-xia, ZHAO Zhi-gang, LI Qiang, LV Hui-xian and LI Ming-sheng. Improved Locality and Similarity Preserving Feature Selection Algorithm [J]. Computer Science, 2020, 47(6A): 480-484.
[7] WANG Cheng-zhang, BAI Xiao-ming, DU Jin-li. Diffuse Interface Based Unsupervised Images Clustering Algorithm [J]. Computer Science, 2020, 47(5): 149-153.
[8] LUO Yue-tong,BIAN Jing-shuai,ZHANG Meng,RAO Yong-ming,YAN Feng. Detection Method of Chip Surface Weak Defect Based on Convolution Denoising Auto-encoders [J]. Computer Science, 2020, 47(2): 118-125.
[9] ZHOU Chang, LI Xiang-li, LI Qiao-lin, ZHU Dan-dan, CHEN Shi-lian, JIANG Li-rong. Sparse Non-negative Matrix Factorization Algorithm Based on Cosine Similarity [J]. Computer Science, 2020, 47(10): 108-113.
[10] FENG Xue. Natural Language Querying with External Semantic Enrichment [J]. Computer Science, 2019, 46(8): 272-276.
[11] CHEN Shen-jin, XUE Yang. Short-term Bus Passenger Flow Prediction Based on Improved Convolutional Neural Network [J]. Computer Science, 2019, 46(5): 175-184.
[12] ZENG Fan-zhi, ZHOU Yan, YU Jia-hao, LUO Yue, QIU Teng-da, QIAN Jie-chang. End-to-End Retrieval Algorithm of Two-dimensional Engineering CAD Model Based on Unsupervised Learning [J]. Computer Science, 2019, 46(12): 298-305.
[13] WU Jing-feng, JIN Wei-dong and TANG Peng. Survey on Monitoring Techniques for Data Abnormalities [J]. Computer Science, 2017, 44(Z11): 24-28.
[14] LI Feng and XIE Si-hong. Study on Abnormal Diagnosis of Moving ECG Signals Based on Unsupervised Learning [J]. Computer Science, 2017, 44(Z11): 68-71.
[15] LIU Xuan, WANG Guo-yin and LUO Xiao-bo. Multi-granularity Clustering of Remote Sensing Image Based on Gaussian Cloud Transformation [J]. Computer Science, 2017, 44(9): 23-27.
Full text



No Suggested Reading articles found!