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Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI

  • Gabriel MaicasEmail author
  • Gustavo Carneiro
  • Andrew P. Bradley
  • Jacinto C. Nascimento
  • Ian Reid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

We present a novel methodology for the automated detection of breast lesions from dynamic contrast-enhanced magnetic resonance volumes (DCE-MRI). Our method, based on deep reinforcement learning, significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy.

This speed-up is achieved via an attention mechanism that progressively focuses the search for a lesion (or lesions) on the appropriate region(s) of the input volume. The attention mechanism is implemented by training an artificial agent to learn a search policy, which is then exploited during inference. Specifically, we extend the deep Q-network approach, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size. We demonstrate our results on a dataset containing 117 DCE-MRI volumes, validating run-time and accuracy of lesion detection.

Keywords

Deep Q-learning Q-net Reinforcement learning Breast lesion detection Magnetic resonance imaging 

References

  1. 1.
    Smith, R.A., Andrews, K., Brooks, D., et al.: Cancer screening in the United States, 2016: a review of current American cancer society guidelines and current issues in cancer screening. CA Cancer J. Clin. 66, 96–114 (2016)CrossRefGoogle Scholar
  2. 2.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA Cancer J. Clin. 66(1), 7–30 (2016)CrossRefGoogle Scholar
  3. 3.
    Siu, A.L.: Screening for breast cancer: US preventive services task force recommendation statement. Ann. Intern. Med. 164, 279–296 (2016)CrossRefGoogle Scholar
  4. 4.
    Gubern-Mérida, A., Martí, R., Melendez, J., et al.: Automated localization of breast cancer in DCE-MRI. Med. Image Anal. 20(1), 265–274 (2015)CrossRefGoogle Scholar
  5. 5.
    McClymont, D., Mehnert, A., Trakic, A., et al.: Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph. JMRI 39(4), 795–804 (2014)CrossRefGoogle Scholar
  6. 6.
    Maicas, G., Carneiro, G., Bradley, A.P.: Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In: 14th International Symposium on Biomedical Imaging (ISBI), pp. 305–309. IEEE (2017)Google Scholar
  7. 7.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  8. 8.
    Caicedo, J.C., Lazebnik, S.: Active object localization with deep reinforcement learning. In: CVPR, pp. 2488–2496 (2015)Google Scholar
  9. 9.
    Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 197–205. Springer, Cham (2016). doi: 10.1007/978-3-319-46976-8_21 CrossRefGoogle Scholar
  10. 10.
    Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)CrossRefGoogle Scholar
  11. 11.
    Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229–237. Springer, Cham (2016). doi: 10.1007/978-3-319-46726-9_27 CrossRefGoogle Scholar
  12. 12.
    Vignati, A., Giannini, V., De Luca, M., et al.: Performance of a fully automatic lesion detection system for breast DCE-MRI. JMRI 34(6), 1341–1351 (2011)CrossRefGoogle Scholar
  13. 13.
    Renz, D.M., Böttcher, J., Diekmann, F., et al.: Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI. JMRI 35(5), 1077–1088 (2012)CrossRefGoogle Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)Google Scholar
  15. 15.
    Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646–661. Springer, Cham (2016). doi: 10.1007/978-3-319-46493-0_39 CrossRefGoogle Scholar
  16. 16.
    Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: DICTA. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gabriel Maicas
    • 1
    Email author
  • Gustavo Carneiro
    • 1
  • Andrew P. Bradley
    • 2
  • Jacinto C. Nascimento
    • 3
  • Ian Reid
    • 1
  1. 1.School of Computer Science, ACVTThe University of AdelaideAdelaideAustralia
  2. 2.School of ITEEThe University of QueenslandBrisbaneAustralia
  3. 3.Institute for Systems and RoboticsInstituto Superior TecnicoLisbonPortugal

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