GraphX\(^\mathbf{\small NET } -\) Chest X-Ray Classification Under Extreme Minimal Supervision

  • Angelica I. Aviles-RiveroEmail author
  • Nicolas Papadakis
  • Ruoteng Li
  • Philip Sellars
  • Qingnan Fan
  • Robby T. Tan
  • Carola-Bibiane Schönlieb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.


Semi-supervised learning Classification Chest X-Ray Graphs Transductive learning 



AIAI is supported by the CMIH, University of Cambridge. NP is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant No 777826. CBS acknowledges Leverhulme Trust (Breaking the non-convexity barrier), the Philip Leverhulme Prize, the EPSRC grants EP/M00483X/1 and EP/N014588/1, the European Union Horizon 2020, the Marie Skodowska-Curie grant 777826 NoMADS and 691070 CHiPS, the CCIMI and the Alan Turing Institute.


  1. 1.
    Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: International Symposium on Biomedical Imaging (ISBI), pp. 294–297 (2015)Google Scholar
  2. 2.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)CrossRefGoogle Scholar
  3. 3.
    Bresson, X., Laurent, T., Uminsky, D., Von Brecht, J.: Multiclass total variation clustering. In: Advances in Neural Information Processing Systems (2013)Google Scholar
  4. 4.
    Bruno, M.A., Walker, E.A., Abujudeh, H.H.: Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35(6), 1668–1676 (2015)CrossRefGoogle Scholar
  5. 5.
    Bühler, T., Hein, M.: Spectral clustering based on the graph p-Laplacian. In: International Conference on Machine Learning (ICML) (2009)Google Scholar
  6. 6.
    Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120–145 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chen, H., Li, K., Zhu, D.E.A.: Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. IEEE Trans. Med. Imaging (TMI) 32, 1576–1586 (2013)CrossRefGoogle Scholar
  8. 8.
    Dodero, L., Gozzi, A., Liska, A., Murino, V., Sona, D.: Group-wise functional community detection through joint laplacian diagonalization. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 708–715. Springer, Cham (2014). Scholar
  9. 9.
    Feld, T.M., Aujol, J.F., Gilboa, G., Papadakis, N.: Rayleigh quotient minimization for absolutely one-homogeneous functionals. Inverse Prob. 35, 064003 (2019)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Folio, L.R.: Chest Imaging: An Algorithmic Approach to Learning. Springer, New York (2012). Scholar
  11. 11.
    Gao, Y., Adeli-M., E., Kim, M., Giannakopoulos, P., Haller, S., Shen, D.: Medical image retrieval using multi-graph learning for MCI diagnostic assistance. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 86–93. Springer, Cham (2015). Scholar
  12. 12.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  13. 13.
    Hein, M., Setzer, S., Jost, L., Rangapuram, S.S.: The total variation on hypergraphs-learning on hypergraphs revisited. In: Advances in Neural Information Processing Systems (2013)Google Scholar
  14. 14.
    Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30, 392–399 (2017)CrossRefGoogle Scholar
  15. 15.
    Moradi, E., Pepe, A., Alzheimer’s Disease Neuroimaging Initiative et al.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015)CrossRefGoogle Scholar
  16. 16.
    Toriwaki, J.I., Suenaga, Y., Negoro, T., Fukumura, T.: Pattern recognition of chest x-ray images. Comput. Graph. Image Process. 2, 252–271 (1973)CrossRefGoogle Scholar
  17. 17.
    Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-Ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2106 (2017)Google Scholar
  18. 18.
    Wang, Z., et al.: Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 291–299. Springer, Cham (2016). Scholar
  19. 19.
    Yao, L., Prosky, J., Poblenz, E., Covington, B., Lyman, K.: Weakly supervised medical diagnosis and localization from multiple resolutions. arXiv preprint arXiv:1803.07703 (2018)
  20. 20.
    Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using Gaussian fields and harmonic functions. In: International conference on Machine learning (ICML), pp. 912–919 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Angelica I. Aviles-Rivero
    • 1
    Email author
  • Nicolas Papadakis
    • 2
  • Ruoteng Li
    • 3
  • Philip Sellars
    • 1
  • Qingnan Fan
    • 4
  • Robby T. Tan
    • 3
    • 5
  • Carola-Bibiane Schönlieb
    • 1
  1. 1.DPMMS and DAMPT, Faculty of MathematicsUniversity of CambridgeCambridgeUK
  2. 2.CNRS, Universite de BordeauxTalenceFrance
  3. 3.National University of SingaporeSingaporeSingapore
  4. 4.Stanford UniversityStanfordUSA
  5. 5.Yale-NUS CollegeSingaporeSingapore

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