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

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

<h4>Objectives</h4>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.<h4>Methods</h4>Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test d...

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Bibliographic Details
Main Authors: Daiju Ueda, Akira Yamamoto, Naoyoshi Onoda, Tsutomu Takashima, Satoru Noda, Shinichiro Kashiwagi, Tamami Morisaki, Shinya Fukumoto, Masatsugu Shiba, Mina Morimura, Taro Shimono, Ken Kageyama, Hiroyuki Tatekawa, Kazuki Murai, Takashi Honjo, Akitoshi Shimazaki, Daijiro Kabata, Yukio Miki
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0265751

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https://doi.org/10.1371/journal.pone.0265751

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