Search Results - "Antropova, Natalia"

A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets by Antropova, Natalia, Huynh, Benjamin Q., Giger, Maryellen L.

“…Background Deep learning methods for radiomics/computer‐aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for…”
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Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms by Li, Hui, Giger, Maryellen L, Huynh, Benjamin Q, Antropova, Natalia O

“…To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract…”
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Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer by Drukker, Karen, Li, Hui, Antropova, Natalia, Edwards, Alexandra, Papaioannou, John, Giger, Maryellen L

“…The hypothesis of this study was that MRI-based radiomics has the ability to predict recurrence-free survival "early on" in breast cancer neoadjuvant…”
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Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks by Antropova, Natalia, Abe, Hiroyuki, Giger, Maryellen L

“…Deep learning methods have been shown to improve breast cancer diagnostic and prognostic decisions based on selected slices of dynamic contrast-enhanced…”
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Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks by Antropova, Natalia, Huynh, Benjamin, Li, Hui, Giger, Maryellen L

“…We present a breast lesion classification methodology, based on four-dimensional (4-D) dynamic contrast-enhanced magnetic resonance images (DCE-MRI), using…”
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Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy by Antropova, Natalia

“…Breast cancer is found in one in eight women in the United States and is expected to be the most frequently diagnosed form of cancer among them in 2018…”
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Multi-task Learning in the Computerized Diagnosis of Breast Cancer on DCE-MRIs by Antropova, Natalia, Huynh, Benjamin, Giger, Maryellen

“…Hand-crafted features extracted from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) have shown strong predictive abilities in characterization…”
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Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 by Antropova, Natalia, Beam, Andrew L, Beaulieu-Jones, Brett K, Chen, Irene, Chivers, Corey, Dalca, Adrian, Finlayson, Sam, Fiterau, Madalina, Fries, Jason Alan, Ghassemi, Marzyeh, Hughes, Mike, Jedynak, Bruno, Kandola, Jasvinder S, McDermott, Matthew, Naumann, Tristan, Schulam, Peter, Shamout, Farah, Yahi, Alexandre

“…This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems…”
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