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pro vyhledávání: '"Miu Sakaida"'
Autor:
Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori, Kenji Hirata, Kohsuke Kudo
Publikováno v:
Applied Sciences, Vol 14, Iss 14, p 5968 (2024)
Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for au
Externí odkaz:
https://doaj.org/article/bb9a394f994f4b388a14ae81747d0c0e
Publikováno v:
Algorithms, Vol 16, Iss 10, p 483 (2023)
Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution wh
Externí odkaz:
https://doaj.org/article/d787eaa478b94b6d81908341a296c20f