Advertisement

Neural Architecture Search for Adversarial Medical Image Segmentation

  • Nanqing DongEmail author
  • Min Xu
  • Xiaodan Liang
  • Yiliang Jiang
  • Wei Dai
  • Eric Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Adversarial training has led to breakthroughs in many medical image segmentation tasks. The network architecture design of the adversarial networks needs to leverage human expertise. Despite the fact that discriminator plays an important role in the training process, it is still unclear how to design an optimal discriminator. In this work, we propose a neural architecture search framework for adversarial medical image segmentation. We automate the process of neural architecture design for the discriminator with continuous relaxation and gradient-based optimization. We empirically analyze and evaluate the proposed framework in the task of chest organ segmentation and explore the potential of automated machine learning in medical applications. We further release a benchmark dataset for chest organ segmentation.

Keywords

Neural architecture search Adversarial networks Medical image segmentation 

References

  1. 1.
    Baker, N., Lu, H., Erlikhman, G., Kellman, P.J.: Deep convolutional networks do not classify based on global object shape. PLoS Comput. Biol. 14(12), e1006613 (2018)CrossRefGoogle Scholar
  2. 2.
    Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., Xing, E.P.: SCAN: structure correcting adversarial network for organ segmentation in chest X-rays. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 263–273. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00889-5_30CrossRefGoogle Scholar
  3. 3.
    Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., Xing, E.: Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 544–552. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_61CrossRefGoogle Scholar
  4. 4.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)Google Scholar
  5. 5.
    Han, Z., Wei, B., Mercado, A., Leung, S., Li, S.: Spine-GAN: semantic segmentation of multiple spinal structures. Med. Image Anal. 50, 23–35 (2018)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)Google Scholar
  7. 7.
    Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: ICLR (2019)Google Scholar
  8. 8.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  9. 9.
    Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. In: NIPS Adversarial Training Workshop (2016)Google Scholar
  10. 10.
    Moeskops, P., Veta, M., Lafarge, M.W., Eppenhof, K.A.J., Pluim, J.P.W.: Adversarial training and dilated convolutions for brain MRI segmentation. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 56–64. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67558-9_7CrossRefGoogle Scholar
  11. 11.
    Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. In: ICML, pp. 4092–4101 (2018)Google Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  13. 13.
    Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71–74 (2000)CrossRefGoogle Scholar
  14. 14.
    Xie, L., Yuille, A.: Genetic CNN. In: ICCV, pp. 1379–1388 (2017)Google Scholar
  15. 15.
    Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: ICLR (2017)Google Scholar
  16. 16.
    Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: CVPR, pp. 8697–8710 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nanqing Dong
    • 1
    • 2
    Email author
  • Min Xu
    • 1
    • 3
  • Xiaodan Liang
    • 1
    • 4
  • Yiliang Jiang
    • 5
  • Wei Dai
    • 6
  • Eric Xing
    • 1
  1. 1.Petuum, Inc.PittsburghUSA
  2. 2.University of OxfordOxfordUK
  3. 3.Carnegie Mellon UniversityPittsburghUSA
  4. 4.Sun Yat-sen UniversityGuangzhouChina
  5. 5.New York UniversityNew York CityUSA
  6. 6.Apple Inc.SeattleUSA

Personalised recommendations