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IMAML-IDCG: Optimization-based meta-learning with ImageNet feature reusing for few-shot invasive ductal carcinoma grading
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long, Malaysia.ORCID iD: 0000-0003-0568-7406
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long, Malaysia.ORCID iD: 0000-0002-9657-8311
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long, Malaysia.ORCID iD: 0000-0002-0263-6358
Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long, Malaysia.ORCID iD: 0000-0002-0007-6174
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2024 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 257, article id 124969Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 257, article id 124969
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Machine Learning
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URN: urn:nbn:se:ltu:diva-108468DOI: 10.1016/j.eswa.2024.124969ISI: 001295417200001Scopus ID: 2-s2.0-85200987898OAI: oai:DiVA.org:ltu-108468DiVA, id: diva2:1887010
Note

Validerad;2024;Nivå 2;2024-08-15 (signyg);

Funder: Universiti Tunku Abdul Rahman Research Fund (Reference: IPSR/RMC/UTARRF/2022-C1/H01)

Available from: 2024-08-06 Created: 2024-08-06 Last updated: 2024-11-20Bibliographically approved

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Mokayed, Hamam

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