ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

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dc.contributor.authorLiu, F.
dc.contributor.authorTian, Y.
dc.contributor.authorChen, Y.
dc.contributor.authorLiu, Y.
dc.contributor.authorBelagiannis, V.
dc.contributor.authorCarneiro, G.
dc.contributor.conferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2022 - 24 Jun 2022 : New Orleans, Louisiana)
dc.date.issued2022
dc.description.abstractEffective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence). One strategy to explore in SSL MIA is based on the pseudo labelling strategy, but it has a few shortcomings. Pseudo-labelling has in general lower accuracy than consistency learning, it is not specifically design for both multi-class and multi-label problems, and it can be challenged by imbalanced learning. In this paper, unlike traditional methods that select confident pseudo label by threshold, we propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers (improving pseudo label accuracy). We run extensive experiments to evaluate ACPL on two public medical image classification benchmarks: Chest X-Ray14 for thorax disease multi-label classification and ISIC2018 for skin lesion multi-class classification. Our method outperforms previous SOTA SSL methods on both datasets¹².
dc.description.statementofresponsibilityFengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro
dc.identifier.citationProceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022, vol.2022-June, pp.20665-20674
dc.identifier.doi10.1109/CVPR52688.2022.02004
dc.identifier.isbn9781665469463
dc.identifier.issn1063-6919
dc.identifier.orcidLiu, F. [0000-0003-0355-2006]
dc.identifier.orcidChen, Y. [0000-0002-8983-2895]
dc.identifier.orcidLiu, Y. [0000-0002-1673-9809]
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]
dc.identifier.urihttps://hdl.handle.net/2440/137559
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/DP180103232
dc.relation.granthttp://purl.org/au-research/grants/arc/FT190100525
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognition
dc.rights©2022 IEEE
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding
dc.titleACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
dc.typeConference paper
pubs.publication-statusPublished

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