Ms. Natalia O. Antropova Profile

Natalia O. Antropova

Doctoral Candidate

SPIE Involvement:

Author

Publications (7)

SPIE Journal Paper | 21 August 2018

Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Natalia Antropova, Benjamin Huynh, Hui Li, Maryellen L. Giger

JMI, Vol. 6, Issue 01, 011002, (August 2018) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.011002

KEYWORDS: Magnetic resonance imaging, Breast, Image classification, Magnetism, Feature extraction, RGB color model, Tumors, 3D image processing, Tumor growth modeling, Convolutional neural networks

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Proceedings Article | 27 February 2018 Paper

Recurrent neural networks for breast lesion classification based on DCE-MRIs

Natasha Antropova, Benjamin Huynh, Maryellen Giger

Proceedings Volume 10575, 105752M (2018) https://doi.org/10.1117/12.2293265

KEYWORDS: Breast, Magnetic resonance imaging, Image classification, Neural networks, Breast cancer, Convolutional neural networks, Medical imaging

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SPIE Journal Paper | 5 February 2018 Open Access

Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks

Natalia Antropova, Hiroyuki Abe, Maryellen Giger

JMI, Vol. 5, Issue 01, 014503, (February 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.014503

KEYWORDS: Magnetic resonance imaging, Feature extraction, Breast, Image classification, Medical imaging, Convolutional neural networks, Breast cancer, Tumors, Diagnostics, Image segmentation

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SPIE Journal Paper | 13 September 2017

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms

Hui Li, Maryellen Giger, Benjamin Q. Huynh, Natasha Antropova

JMI, Vol. 4, Issue 04, 041304, (September 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.041304

KEYWORDS: Breast cancer, Cancer, Feature extraction, Convolutional neural networks, Mammography, Breast, Medical imaging, Lithium, Image classification, Feature selection

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Proceedings Article | 3 March 2017 Presentation + Paper

Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI

Natasha Antropova, Benjamin Huynh, Maryellen Giger

Proceedings Volume 10134, 101341G (2017) https://doi.org/10.1117/12.2255582

KEYWORDS: Convolutional neural networks, Analytical research, Breast, Neural networks, Feature extraction, Computer aided diagnosis and therapy, Image analysis, Machine learning, Radiology, Magnetic resonance imaging

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Proceedings Article | 3 March 2017 Presentation + Paper

Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning

Benjamin Huynh, Natasha Antropova, Maryellen Giger

Proceedings Volume 10134, 101340U (2017) https://doi.org/10.1117/12.2255316

KEYWORDS: Neural networks, Feature extraction, Breast, Breast cancer, Computer aided diagnosis and therapy, Image analysis, Machine learning, Convolutional neural networks, Receivers, RGB color model, Medical imaging, Magnetic resonance imaging

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Proceedings Article | 22 March 2016 Paper

Efficient iterative image reconstruction algorithm for dedicated breast CT

Natalia Antropova, Adrian Sanchez, Ingrid Reiser, Emil Sidky, John Boone, Xiaochuan Pan

Proceedings Volume 9783, 97834K (2016) https://doi.org/10.1117/12.2216933

KEYWORDS: Reconstruction algorithms, Breast, X-ray computed tomography, Image restoration, Image enhancement, Breast cancer, Mammography, X-rays, Image quality, Prototyping, Algorithm development, Data modeling, Computed tomography

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Showing 5 of 7 publications

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