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Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.

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第一作者： Seunghyun,Gwak

第一单位： Computational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.

作者： Seunghyun,Gwak ^[1] ; Sooyoung,Yang ^[2] ; Heawon,Jeong ^[1] ; Junhu,Park ^[1] ; Myungjoo,Kang ^[3]

作者单位： Computational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea. ^[1] Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea. ^[2] Computational Science & Technology, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.;Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea.;Department of Mathematical Sciences and Research Institute of Mathmatics, Seoul National University, 1 Gwanak-ro, Seoul, Gwanak-gu 08826 South Korea. ^[3]

关键词 COVID-19 diagnosis Computed tomography Image classificiation Masked autoencoder Sparse-view sinogram

DOI 10.1007/s13534-025-00478-4

PMID 40621608

发布时间 2025-07-08

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Biomedical engineering letters

2025年15卷4期

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Biomedical engineering letters

2025年15卷4期

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Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.

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