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

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
Source :
PLoS ONE, Vol 17, Iss 3, p e0265751 (2022)
Publication Year :
2022
Publisher :
Public Library of Science (PLoS), 2022.

Abstract

ObjectivesThe 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.MethodsMammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model's sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets.ResultsThe hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45-0.47 mFPI and had partial AUCs of 0.93 in both test datasets.ConclusionsThe DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
17
Issue :
3
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.949bf8a8f4e34d038679f6f1ac589d22
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pone.0265751