Artigos com autorizações de acesso público - Natasha AntropovaSaiba mais
4 artigos não disponíveis publicamente
Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning
BQ Huynh, N Antropova, ML Giger
Medical imaging 2017: computer-aided diagnosis 10134, 207-213, 2017
Autorizações: US National Institutes of Health
Recurrent neural networks for breast lesion classification based on DCE-MRIs
N Antropova, B Huynh, M Giger
Medical imaging 2018: Computer-aided diagnosis 10575, 593-598, 2018
Autorizações: US National Institutes of Health
Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI
N Antropova, B Huynh, M Giger
Medical imaging 2017: Computer-aided diagnosis 10134, 369-373, 2017
Autorizações: US National Institutes of Health
Efficient iterative image reconstruction algorithm for dedicated breast CT
N Antropova, A Sanchez, IS Reiser, EY Sidky, J Boone, X Pan
Medical Imaging 2016: Physics of Medical Imaging 9783, 1222-1227, 2016
Autorizações: US National Institutes of Health
6 artigos disponíveis publicamente
International evaluation of an AI system for breast cancer screening
SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, ...
Nature 577 (7788), 89-94, 2020
Autorizações: Cancer Research UK, National Institute for Health Research, UK
A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets
N Antropova, BQ Huynh, ML Giger
Medical physics 44 (10), 5162-5171, 2017
Autorizações: US National Institutes of Health
Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms
H Li, ML Giger, BQ Huynh, NO Antropova
Journal of medical imaging 4 (4), 041304-041304, 2017
Autorizações: US National Institutes of Health
Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks
N Antropova, H Abe, ML Giger
Journal of Medical Imaging 5 (1), 014503-014503, 2018
Autorizações: US National Institutes of Health
Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival “early on” in neoadjuvant treatment of breast cancer
K Drukker, H Li, N Antropova, A Edwards, J Papaioannou, ML Giger
Cancer imaging 18, 1-9, 2018
Autorizações: US National Institutes of Health
Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks
N Antropova, B Huynh, H Li, ML Giger
Journal of Medical Imaging 6 (1), 011002-011002, 2019
Autorizações: US National Institutes of Health
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