Multi-scale Convolutional Neural Networks for Lung Nodule Classification

  • Wei Shen
  • Mu Zhou
  • Feng YangEmail author
  • Caiyun Yang
  • Jie TianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)


We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior definition of nodule morphology. We propose a hierarchical learning framework—Multi-scale Convolutional Neural Networks (MCNN)—to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules without nodule segmentation.


Lung nodule classification Computed Tomography (CT) Imaging Convolutional Neural Networks Computer-Aided Diagnoses (CAD) 



The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. This paper is supported by the Chinese Academy of Sciences Key Deployment Program under Grant No. KGZD-EW-T03, the National Basic Research Program of China (973 Program) under Grant 2011CB707700, the National Natural Science Foundation of China under Grant No. 81227901, 61231004, 81370035, 81230030, 61301002, 61302025, major projects of Biomedicine Department of Shanghai Science and Technology Commission (13411950100), the Chinese Academy of Sciences Fellowship for Young International Scientists under Grant No. 2010Y2GA03, 2013Y1 GA0004, 2013Y1GB0005, the Chinese Academy of Sciences Visiting Professorship for Senior International Scientists under Grant No. 2012T1G0036, 2010T2G 36, 2012T1G0039, 2013T1G0013, the National High Technology Research and Development Program of China (863 Program) under 2012AA021105, the Guangdong Province-Chinese Academy of Sciences comprehensive strategic cooperation program under 2010A090100032 and 2012B090400039, the NSFC-NIH Biomedical collaborative research program under 81261120414, the National Science and Technology Supporting Plan under 2012BAI15B08, the Beijing Natural Science Foundation under Grant No. 4132080, the Fundamental Research Funds for the Central Universities under Grant No. 2013JBZ014.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Key Laboratory of Molecular Imaging of Chinese Academy of SciencesInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  3. 3.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA

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