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• W2899114598 abstract "Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. In this work, we propose a computer-aided diagnosis (CAD) scheme for mammography based on transfer representation learning using the Inception-V3 architecture. We evaluate the performance of the proposed scheme using the INBreast database, where the features are extracted from different layers of the architecture. In order to cope with the small dataset size limitation, we expand the training dataset by generating artificial mammograms and employing different augmentation techniques. The proposed scheme shows great potential with a maximal area under the receiver operating characteristics curve of 0.91." @default.
• W2899114598 created "2018-11-09" @default.
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• W2899114598 date "2018-07-01" @default.
• W2899114598 modified "2023-10-16" @default.
• W2899114598 title "Transfer Representation Learning using Inception-V3 for the Detection of Masses in Mammography" @default.
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• W2899114598 doi "https://doi.org/10.1109/embc.2018.8512750" @default.
• W2899114598 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30440937" @default.
• W2899114598 hasPublicationYear "2018" @default.
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