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

AVILES-RIVERO, Angelica I.; PAPADAKIS, Nicolas; LI, Ruoteng; SELLARS, Philip; FAN, Qingnan; TAN, Robby; SCHÖNLIEB, Carola-Bibiane

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AVILES-RIVERO, Angelica I.
Department of Applied Mathematics and Theoretical Physics [DAMTP]

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

LI, Ruoteng

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Communication dans un congrès

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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19), 2019-10-13, Shenzen. p. 504-512

Resumen en inglés

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.< Leer menos

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https://oskar-bordeaux.fr/handle/20.500.12278/192803

Proyecto europeo

Nonlocal Methods for Arbitrary Data Sources

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Institut de Mathématiques de Bordeaux (IMB) - UMR 5251

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