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

ABSTRACT:

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 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 exter

SUBMITTER: Ueda D 

PROVIDER: S-EPMC8947392 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature