Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169033
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dc.contributor.authorFeng, Yuanyien_US
dc.contributor.authorLuo, Yuemeien_US
dc.contributor.authorYang, Jianfeien_US
dc.date.accessioned2023-06-27T06:06:40Z-
dc.date.available2023-06-27T06:06:40Z-
dc.date.issued2023-
dc.identifier.citationFeng, Y., Luo, Y. & Yang, J. (2023). Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation. Knowledge-Based Systems, 264, 110324-. https://dx.doi.org/10.1016/j.knosys.2023.110324en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/169033-
dc.description.abstractIn the wake of the Coronavirus disease (COVID-19) pandemic, chest computed tomography (CT) has become an invaluable component in the rapid and accurate detection of COVID-19. CT scans traditionally require manual inspections from medical professionals, which is expensive and tedious. With advancements in machine learning, deep neural networks have been applied to classify CT scans for efficient diagnosis. However, three challenges hinder this application of deep learning: (1) Domain shift across CT platforms and human subjects impedes the performance of neural networks in different hospitals. (2) Unsupervised Domain Adaptation (UDA), the traditional method to overcome domain shift, typically requires access to both source and target data. This is not realistic in COVID-19 diagnosis due to the sensitivity of medical data. The privacy of patients must be protected. (3) Data imbalance may exist between easy/hard samples and between data classes which can overwhelm the training of deep networks, causing degenerate models. To overcome these challenges, we propose a Cross-Platform Privacy-Preserving COVID-19 diagnosis network (CP 3 Net) that integrates domain adaptation, self-supervised learning, imbalanced label learning, and rotation classifier training into one synergistic framework. We also create a new CT benchmark by combining real-world datasets from multiple medical platforms to facilitate the cross-domain evaluation of our method. Through extensive experiments, we demonstrate that CP 3 Net outperforms many popular UDA methods and achieves state-of-the-art results in diagnosing COVID-19 using CT scans.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation.ispartofKnowledge-Based systemsen_US
dc.rights© 2023 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleCross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.knosys.2023.110324-
dc.identifier.pmid36713615-
dc.identifier.scopus2-s2.0-85147848362-
dc.identifier.volume264en_US
dc.identifier.spage110324en_US
dc.subject.keywordsDomain Adaptationen_US
dc.subject.keywordsDeep Learningen_US
dc.description.acknowledgementThis work is supported by NTU Presidential Postdoctoral Fellowship, ‘‘Adaptive Multimodal Learning for Robust Sensing and Recognition in Smart Cities’’ project fund, at Nanyang Technological University, Singapore.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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