Naidoo, Preshen ORCID: https://orcid.org/0009-0004-8328-6194, Fernandes, Patricia
ORCID: https://orcid.org/0009-0000-9720-2829, Ufumaka, Isreal
ORCID: https://orcid.org/0000-0002-8906-4822, Serej, Nasim Dadashi
ORCID: https://orcid.org/0000-0002-2898-1926, Howard, James
ORCID: https://orcid.org/0000-0002-9989-6331, Francis, Darrel
ORCID: https://orcid.org/0000-0002-3410-0814, Manisty, Charlotte
ORCID: https://orcid.org/0000-0003-0245-7090 and Zolgharni, Massoud
ORCID: https://orcid.org/0000-0003-0904-2904
(2025)
Spatiotemporal contrastive learning for Echocardiography View Classification.
In: International Conference on AI in Healthcare, 8-10 September 2025, Cambridge, United Kingdom.
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Abstract
Echocardiographic view classification is essential for accurate cardiac assessments, yet it remains challenging due to anatomical overlap, operator variability, motion artifacts, image quality issues, and dataset limitations. Deep learning methods could address these issues by incorporating temporal models, representation learning, and domain adaptation to improve classification robustness. This study proposes a contrastive representation learning framework that integrates temporal and spatial augmentation strategies, to learn more robust and invariant feature representations. Experimental results demonstrate that the proposed approach achieves an accuracy of 96.4%, surpassing previous methods. The findings indicate that the model effectively captures robust and invariant feature representations, strengthening its ability to distinguish between echocardiographic views and consequently enhancing classification performance.
Item Type: | Conference or Workshop Item (Paper) |
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ISBN: | 9783032006561 |
Identifier: | 10.1007/978-3-032-00656-1_18 |
Page Range: | pp. 247-260 |
Identifier: | 10.1007/978-3-032-00656-1_18 |
Keywords: | Contrastive learning; View classification; Representation learning; Echocardiography |
Subjects: | Computing > Intelligent systems |
Date Deposited: | 03 Oct 2025 12:37 |
Last Modified: | 03 Oct 2025 15:00 |
URI: | https://repository.uwl.ac.uk/id/eprint/14145 | Sustainable Development Goals: | Goal 3: Good Health and Well-Being |
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