GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [DAMTP] | |
dc.contributor.author | AVILES-RIVERO, Angelica I. | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | PAPADAKIS, Nicolas | |
dc.contributor.author | LI, Ruoteng | |
dc.contributor.author | SELLARS, Philip | |
dc.contributor.author | FAN, Qingnan | |
dc.contributor.author | TAN, Robby | |
hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [DAMTP] | |
dc.contributor.author | SCHÖNLIEB, Carola-Bibiane | |
dc.date.accessioned | 2024-04-04T03:00:15Z | |
dc.date.available | 2024-04-04T03:00:15Z | |
dc.date.conference | 2019-10-13 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192803 | |
dc.description.abstractEn | 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. | |
dc.language.iso | en | |
dc.title | GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision | |
dc.type | Communication dans un congrès | |
dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
dc.identifier.arxiv | 1907.10085 | |
dc.description.sponsorshipEurope | Nonlocal Methods for Arbitrary Data Sources | |
bordeaux.page | 504-512 | |
bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
bordeaux.conference.title | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19) | |
bordeaux.country | CN | |
bordeaux.conference.city | Shenzen | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-02193970 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | oui | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-02193970v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.title=GraphX$%5E%7BNET%7D-$%20Chest%20X-Ray%20Classification%20Under%20Extreme%20Minimal%20Supervision&rft.atitle=GraphX$%5E%7BNET%7D-$%20Chest%20X-Ray%20Classification%20Under%20Extreme%20Minimal%20Supervision&rft.spage=504-512&rft.epage=504-512&rft.au=AVILES-RIVERO,%20Angelica%20I.&PAPADAKIS,%20Nicolas&LI,%20Ruoteng&SELLARS,%20Philip&FAN,%20Qingnan&rft.genre=unknown |
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