Pulmonary nodule classification with deep residual networks

Original Article
  • 196 Downloads

Abstract

Purpose 

Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules.

Methods

We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification.

Results

Due to a lack of public datasets with standardized problem definitions and train/test splits, studies in this area tend to not compare directly against other existing work. This makes it hard to know the relative improvement in the new solution. In contrast, we directly compare our system against two state-of-the-art deep learning systems for nodule classification on the LIDC/IDRI dataset using the same experimental setup and data set. The results show that our system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy.

Conclusions

The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy. This reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains.

Keywords

Lung nodule Convolutional neural network CT images 

References

  1. 1.
    American Cancer Society: Cancer facts & figures 2016 (2016). http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-047079.pdf. Last accessed 23 Aug 2016
  2. 2.
    Bengio Y, Louradour J, Collobert R, Weston J (2009) Curriculum learning. In: Proceedings of 26th ICML, pp 41–48Google Scholar
  3. 3.
    Ciompi F, de Hoop B, van Riel SJ, Chung K, Scholten ET, Oudkerk M, de Jong PA, Prokop M, van Ginneken B (2015) Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med Image Anal 26(1):195–202CrossRefPubMedGoogle Scholar
  4. 4.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36CrossRefPubMedGoogle Scholar
  5. 5.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 29th IEEE CVPRGoogle Scholar
  6. 6.
    He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. arXiv Preprint arXiv:1603.05027
  7. 7.
    Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther 8:2015–2022Google Scholar
  8. 8.
    International Agency for Research on Cancer (2012) Estimated incidence, mortality and prevalence worldwide in 2012. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=lung. Last accessed 23 Aug 2016
  9. 9.
    Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of 32nd ICML, pp 448–456Google Scholar
  10. 10.
    Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of EMNLP 2014Google Scholar
  11. 11.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105Google Scholar
  12. 12.
    Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT images. In: 12th Conference on Computer and Robot Vision, pp. 133–138Google Scholar
  13. 13.
    Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209CrossRefPubMedGoogle Scholar
  14. 14.
    LeCun Y, Boser B, Denker JS, Howard RE, Habbard W, Jackel LD, Henderson D (1990) Handwritten digit recognition with a back-propagation network. Adv Neural Inf Process Syst 2:396–404Google Scholar
  15. 15.
    Lee MC, Boroczky L, Sungur-Stasik K, Cann AD, Borczuk AC, Kawut SM, Powell CA (2010) Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artif Intell Med 50(1):43–53CrossRefPubMedGoogle Scholar
  16. 16.
    Orozco HM, Villegas OOV, Sánchez VGC, Domínguez HDJO, Alfaro MdJN (2015) Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 14:9CrossRefGoogle Scholar
  17. 17.
    Reeves AP, Biancardi AM (2011) The lung image Database Consortium (LIDC) nodule size report (2011). http://www.via.cornell.edu/lidc/. Last accessed 23 Aug 2016
  18. 18.
    Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) OverFeat: integrated recognition, localization and detection using convolutional networks. In: Proceedings of ICLR 2014Google Scholar
  19. 19.
    Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Sv Riel, Wille MW, Naqibullah M, Sanchez C, Bv Ginneken (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169Google Scholar
  20. 20.
    Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging 9123:588–599Google Scholar
  21. 21.
    Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489CrossRefPubMedGoogle Scholar
  22. 22.
    Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of ICLR 2015Google Scholar
  23. 23.
    Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J (2015) Data from LIDC-IDRI (2015). https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI. Last accessed 23 Aug 2016
  24. 24.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. ArXiv Preprint arXiv:1409.4842
  25. 25.
    Telgarsky M (2016) Benefits of depth in neural networks. arXiv Preprint arXiv:1602.04485
  26. 26.
    Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitudeGoogle Scholar
  27. 27.
    Toshev A, Szegedy C (2014) DeepPose: human pose estimation via deep neural networks. In: Proceedings of CVPR 2014Google Scholar
  28. 28.
    Zeiler MD (2012) ADADELTA: an adaptive learning rate method. arXiv Preprint arXiv:1212.5701
  29. 29.
    Zinovev D, Feigenbaum J, Raicu D, Furst J (2010) Predicting panel ratings for semantic characteristics of lung nodules. Technical Report (2010). http://via.library.depaul.edu/tr/18Google Scholar

Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Computer EngineeringLa Trobe UniversityMelbourneAustralia
  2. 2.Department of Public HealthLa Trobe UniversityMelbourneAustralia

Personalised recommendations