Computer Science ›› 2020, Vol. 47 ›› Issue (9): 129-134.doi: 10.11896/jsjkx.190700203

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability

ZHANG Jia-jia, ZHANG Xiao-hong   

  1. School of Big Data & Software Engineering,Chongqing University,Chongqing 400000,China
  • Received:2019-07-29 Published:2020-09-10
  • About author:ZHANG Jia-jia,born in 1994,postgra-duate.Her main research interests include medical image analysis,deep learning and so on.
    ZHANG Xiao-hong,born in 1973,professor.His main research interests include data mining of software enginee-ring,topic modeling,image semantic analysis and video analysis.
  • Supported by:
    National Natural Science Foundation of China (61772093) and Chongqing Major Theme Projects (cstc2018jszx-cyztzxX0017).

Abstract: The characteristics of lung nodules are complex and diverse,which make it difficult to classify lung nodules.Although more and more deep learning models are applied to the lung nodule classification task of computer-aided lung cancer diagnosis systems,the “black box” characteristics of these models cannot explain what knowledge the model has learned from the data and how the knowledge influences the decision,leading to a lack of reliability in the diagnosis results.To this end,an interpretable multi-branch convolutional neural network model is proposed to identify the benign and malignant lung nodules.The model uses the semantic features of the pulmonary nodules which radiologists use in diagnosis to assist identifying the benign and malignant lung nodules.These characteristics are combined with the branch of malignancy classification into a multi-branch network.Then beyond the malignancy classification,the model can predict nodule attributes,which could potentially explain the diagnosis result.Experimental results on the LIDC-IDRI dataset show that,compared with the existing methods,the proposed model can not only obtain interpretable diagnostic results,but also achieve better classification of lung nodules with an accuracy rate of 97.8%.

Key words: Classification of malignant degree of pulmonary nodules, Computer-aided diagnosis, Convolutional neural network, Interpretable, Multi-branch

CLC Number: 

  • TP183
[1] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2016[J].CA:A Cancer Journal for Clinicians,2016,66(1):7-30.
[2] SHEN S,HAN S X,PETOUSIS P,et al.A Bayesian model for estimating multi-state disease progression[J].Computers in Biology and Medicine,2017,81:111-120.
[3] National Lung Screening Trial Research Team.Reduced lung-cancer mortality with low-dose computed tomographic screening[J].New England Journal of Medicine,2011,365(5):395-409.
[4] ARMATO III S G,ALTMAN M B,WILKIE J,et al.Automated lung nodule classification following automated nodule detection on CT:A serial approach[J].Medical Physics,2003,30(6):1188-1197.
[5] SHEN S,BUI A A T,CONG J,et al.An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy[J].Computers in Biology and Medicine,2015,57:139-149.
[6] DUGGAN N,BAE E,SHEN S,et al.A technique for lung nodule candidate detection in CT using global minimization methods[C]//International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition.Cham:Springer, 2015:478-491.
[7] FIRMINO M,ANGELO G,MORAIS H,et al.Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy[J].Biomedical Engineering Online,2016,15(1):2.
[8] AMIR G J,LEHMANN H P.After detection:The improvedaccuracy of lung cancer assessment using radiologic computer-aided diagnosis[J].Academic Radiology,2016,23(2):186-191.
[9] HUANG P,PARK S,YAN R,et al.Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules:a matched case-control study[J].Radiology,2017,286(1):286-295.
[10] SHEN W,ZHOU M,YANG F,et al.Multi-scale Convolutional Neural Networks for Lung Nodule Classification[J].Inf Process Med Imaging,2015,24:588-599.
[11] SHEN W,ZHOU M,YANG F,et al.Multi-crop Convolutional Neural Networks for Lung Nodule Malignancy Suspiciousness Classification[J].Pattern Recognition,2016,61(61):663-673.
[12] PIEDRA E A R,TAIRA R K,EL-SADEN S,et al.Assessing variability in brain tumor segmentation to improve volumetric accuracy and characterization of change[C]// IEEE-EMBS International Conference on Biomedical & Health Informatics.IEEE,2016.
[13] DOU Q,CHEN H,YU L,et al.Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection[J].IEEE Transactions on Biomedical Engineering,2017,64(7):1558-1567.
[14] LIANG C H,YUE J Y,HAN D M,et al.Diagnostic value of lobulation sign in solitary pulmonary nodule by CT to determine.
benignity or malignancy[J].Medical Information(Section of Operative Surgery),2007(10):94-95.
[15] HUSSEIN S,CAO K,SONG Q,et al.Risk stratification of lung nodules using 3d cnn-based multi-task learning[C]//International Conference on Information Processing in Medical Imaging.Cham:Springer, 2017:249-260.
[16] KIM H,PARK C M,GOO J M,et al.Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer[J].Investigative Radiology,2015,50(9).
[17] ARMATO S,MCLENNAN G,M MCNITT-GRAY,et al.WE-B-201B-02:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI):A Completed Public Database of CT Scans for Lung Nodule Analysis[J].Medical Physics,2010,37(6Part6):3416-3417.
[18] MOLCHANOV P,TYREE S,KARRAS T,et al.Pruning Conv-olutional Neural Networks for Resource Efficient Inference[J].International Conference on Learning Representations,2017,2:324-332.
[19] CAUSEY J,ZHANG J Y,MA S Q,et al.Highly accurate model for prediction of lung nodule malignancy with CT scans[J].Scientific Reports,2018,8(1):9286.
[20] XIE Y,ZHANG J,XIA Y,et al.Fusing texture,shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT[J].Information Fusion,2018,42:102-110.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[3] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[4] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[5] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[6] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[7] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[8] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[9] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[10] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[11] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[12] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[13] ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209.
[14] ZHANG Wen-xuan, WU Qin. Fine-grained Image Classification Based on Multi-branch Attention-augmentation [J]. Computer Science, 2022, 49(5): 105-112.
[15] ZHAO Ren-xing, XU Pin-jie, LIU Yao. ECG-based Atrial Fibrillation Detection Based on Deep Convolutional Residual Neural Network [J]. Computer Science, 2022, 49(5): 186-193.
Full text



No Suggested Reading articles found!