X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approach Article Swipe
Ivan Martinović
,
Shitong Mao
,
Mehdy Dousty
,
W. Li
,
Milena Đukanović
,
Errol Colak
,
Ervin Sejdić
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1139/facets-2024-0229
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1139/facets-2024-0229
Self-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. This paper provides a comprehensive overview of self-supervised learning applications across various X-ray modalities, including conventional X-ray, computed tomography, mammography, and dental X-ray. Apart from the application of self-supervised learning in the interpretation phase of X-ray images, the paper also emphasizes the critical role of self-supervised learning integration in the preprocessing and archiving phase. Furthermore, the paper explores the application of self-supervised learning in multi-modal scenarios, which represents a key future direction in developing machine learning-based applications across the field of medicine. Lastly, the paper addresses the main challenges associated with the development of self-supervised learning applications tailored for X-ray modalities. The findings from the reviewed literature strongly suggest that the self-supervised learning approach has the potential to be a “ game-changer”, enabling the elimination of the current situation where many machine learning-based systems are developed but few are deployed in daily clinical practice.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1139/facets-2024-0229
- OA Status
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- Cited By
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https://doi.org/10.1139/facets-2024-0229Digital Object Identifier
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X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approachWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Ivan Martinović, Shitong Mao, Mehdy Dousty, W. Li, Milena Đukanović, Errol Colak, Ervin SejdićList of authors in order
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https://doi.org/10.1139/facets-2024-0229Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1139/facets-2024-0229Direct OA link when available
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Modalities, Artificial intelligence, Computer science, Machine learning, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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