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https://hdl.handle.net/2440/137559
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Type: | Conference paper |
Title: | ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification |
Author: | Liu, F. Tian, Y. Chen, Y. Liu, Y. Belagiannis, V. Carneiro, G. |
Citation: | Proceedings / 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 |
Publisher: | IEEE |
Issue Date: | 2022 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781665469463 |
ISSN: | 1063-6919 |
Conference Name: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2022 - 24 Jun 2022 : New Orleans, Louisiana) |
Statement of Responsibility: | Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro |
Abstract: | Effective 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. |
Rights: | ©2022 IEEE |
DOI: | 10.1109/CVPR52688.2022.02004 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180103232 http://purl.org/au-research/grants/arc/FT190100525 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding |
Appears in Collections: | Australian Institute for Machine Learning publications Computer Science publications |
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