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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)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

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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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