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GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision

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.
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Contributor : Nicolas Papadakis <>
Submitted on : Thursday, July 25, 2019 - 7:56:49 AM
Last modification on : Monday, October 26, 2020 - 2:50:03 PM


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  • HAL Id : hal-02193970, version 1
  • ARXIV : 1907.10085




Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Qingnan Fan, et al.. GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision. International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2019, Shenzen, China. ⟨hal-02193970⟩



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