An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization is a research paper published in Medical Image Analysis (2020). On theSindex it has a DataRank of 0. It has been cited 205 times.
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National Science Foundation
Grant: 1922658
NRT-HDR: FUTURE Foundations, Translation, and Responsibility for Data Science Impact
National Institutes of Health
Grant: P41EB017183
National Institutes of Health
Grant: R21CA225175
National Institutes of Health
Grant: 3P41EB017183-05S1
Center for Advanced Imaging Innovation and Research (CAI2R)
National Institutes of Health
Grant: 5P41EB017183-05
Center for Advanced Imaging Innovation and Research (CAI2R)
National Institutes of Health
Grant: 1R21CA225175-01A1
Reducing recall rates of screening mammography with deep convolutional neural network
FWCI
16.90
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study
Additional file 1 of Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study
Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM)