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

hal.structure.identifierDepartment of Applied Mathematics and Theoretical Physics [DAMTP]
dc.contributor.authorAVILES-RIVERO, Angelica I.
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
dc.contributor.authorLI, Ruoteng
dc.contributor.authorSELLARS, Philip
dc.contributor.authorFAN, Qingnan
dc.contributor.authorTAN, Robby
hal.structure.identifierDepartment of Applied Mathematics and Theoretical Physics [DAMTP]
dc.contributor.authorSCHÖNLIEB, Carola-Bibiane
dc.date.accessioned2024-04-04T03:00:15Z
dc.date.available2024-04-04T03:00:15Z
dc.date.conference2019-10-13
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192803
dc.description.abstractEnThe 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.isoen
dc.titleGraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.identifier.arxiv1907.10085
dc.description.sponsorshipEuropeNonlocal Methods for Arbitrary Data Sources
bordeaux.page504-512
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'19)
bordeaux.countryCN
bordeaux.conference.cityShenzen
bordeaux.peerReviewedoui
hal.identifierhal-02193970
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02193970v1
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