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.
A.I. Aviles-Rivero and N. Papadakis—Equal Contribution.
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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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Aviles-Rivero, A.I. et al. (2019). GraphX\(^\mathbf{\small NET } -\) Chest X-Ray Classification Under Extreme Minimal Supervision. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_56
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DOI: https://doi.org/10.1007/978-3-030-32226-7_56
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