Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets

OBJECTIVES: The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography. METHODS: Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected...

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Detalles Bibliográficos
Autores principales: Ueda, Daiju, Yamamoto, Akira, Onoda, Naoyoshi, Takashima, Tsutomu, Noda, Satoru, Kashiwagi, Shinichiro, Morisaki, Tamami, Fukumoto, Shinya, Shiba, Masatsugu, Morimura, Mina, Shimono, Taro, Kageyama, Ken, Tatekawa, Hiroyuki, Murai, Kazuki, Honjo, Takashi, Shimazaki, Akitoshi, Kabata, Daijiro, Miki, Yukio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947392/
https://www.ncbi.nlm.nih.gov/pubmed/35324962
http://dx.doi.org/10.1371/journal.pone.0265751

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947392/
https://www.ncbi.nlm.nih.gov/pubmed/35324962
http://dx.doi.org/10.1371/journal.pone.0265751

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