GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
Language
en
Communication dans un congrès
This item was published in
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19), 2019-10-13, Shenzen. p. 504-512
English 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 ...Read more >
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.Read less <
European Project
Nonlocal Methods for Arbitrary Data Sources
Origin
Hal imported