Advertisement

Using 1D Patch-Based Signatures for Efficient Cascaded Classification of Lung Nodules

  • Dario Augusto Borges Oliveira
  • Matheus Palhares Viana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)

Abstract

In the last years, convolutional neural networks (CNN) have been largely used to address a wide range of image analysis problems. In medical imaging, their importance increased exponentially despite of known difficulties in building large annotated training datasets in medicine. When it comes to 3D image exams analysis, 3D convolutional networks commonly represent the state-of-art, but can easily became computationally prohibitive due to the massive amount of data and processing involved. This scenario creates opportunities for methods that deliver competitive results while promoting efficiency in data usage and processing time. In this context, this paper proposes a comprehensive 1D patch-based data representation model to be used in an efficient cascaded approach for lung nodules false positive reduction. The proposed pipeline combines three convolutional networks: a 3D network that uses regular multi-scale volumetric patches, a 2D network that uses a trigonometric bi-dimensional representation of these patches, and a 1D network that uses a very compact 1D patch representation for filtering obvious cases. We run our experiments using the publicly available LUNA challenge dataset and demonstrate that the proposed cascaded approach achieves very competitive results while using up to 55 times less data in average and running around 3.5 times faster in average when compared to regular 3D CNNs.

Keywords

Convolutional neural networks Deep learning Dimension reduction Medical imaging Lung nodules 

References

  1. 1.
    Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)Google Scholar
  2. 2.
    Ciompi, F., et al.: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. arXiv preprint arXiv:1610.09157 (2016)
  3. 3.
    Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A.: Multi-level contextual 3D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. PP(99), 1 (2016)Google Scholar
  4. 4.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42(Supplement C), 60–88 (2017). http://www.sciencedirect.com/science/article/pii/S1361841517301135CrossRefGoogle Scholar
  5. 5.
    Oliveira, D.A.B., Viana, M.P.: An efficient multi-scale data representation method for lung nodule false postive reduction using convolutional neural networks. In: IEEE International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)Google Scholar
  6. 6.
    Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017).  https://doi.org/10.1016/j.media.2017.06.015CrossRefGoogle Scholar
  7. 7.
    Tajbakhsh, N., et al.: convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dario Augusto Borges Oliveira
    • 1
  • Matheus Palhares Viana
    • 1
  1. 1.IBM Research BrazilParaísoBrazil

Personalised recommendations