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Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

  • Yutong Xie
  • Yong XiaEmail author
  • Jianpeng Zhang
  • David Dagan Feng
  • Michael Fulham
  • Weidong Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

The classification of benign versus malignant lung nodules using chest CT plays a pivotal role in the early detection of lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign lung nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of lung nodules, respectively, and jointly utilizes them to classify lung nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches.

Keywords

Lung nodule classification Deep learning Ensemble learning Computed tomography (CT) 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61471297, in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University under Grants Z2017041, and in part by the Australian Research Council (ARC) Grants. We acknowledged 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 work.

Supplementary material

455908_1_En_75_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (docx 1099 kb)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yutong Xie
    • 1
  • Yong Xia
    • 1
    Email author
  • Jianpeng Zhang
    • 1
  • David Dagan Feng
    • 2
    • 5
  • Michael Fulham
    • 2
    • 3
    • 4
  • Weidong Cai
    • 2
  1. 1.Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information TechnologiesUniversity of SydneySydneyAustralia
  3. 3.Department of Molecular ImagingRoyal Prince Alfred HospitalCamperdownAustralia
  4. 4.Sydney Medical SchoolUniversity of SydneySydneyAustralia
  5. 5.Med-X Research InstituteShanghai Jiaotong UniversityShanghaiChina

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