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Natasha Antropova
Natasha Antropova
DeepMind
Zweryfikowany adres z google.com
Tytuł
Cytowane przez
Cytowane przez
Rok
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
12902020
Protein complex prediction with AlphaFold-Multimer
R Evans, M O'Neill, A Pritzel, N Antropova, AW Senior, T Green, A Žídek, ...
BioRxiv, 2021
3182021
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
2752017
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, 2017
672017
Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks
NO Antropova, H Abe, ML Giger
Journal of Medical Imaging 5 (1), 014503, 2018
632018
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), 1-9, 2018
472018
SU‐D‐207B‐06: Predicting breast cancer malignancy on DCE‐MRI data using pre‐trained convolutional neural networks
N Antropova, B Huynh, M Giger
Medical physics 43 (6Part4), 3349-3350, 2016
362016
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
252017
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, 2018
232018
Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021
R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ...
Google Scholar, 0
21
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
142017
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
112018
Protein complex prediction with AlphaFold-Multimer. bioRxiv [Preprint](2021)
R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green
October, 0
8
Augustin žídek, Russell Bates, Sam Blackwell, Jason Yim, et al. Protein complex prediction with alphafold-multimer
R Evans, M O’Neill, A Pritzel, N Antropova, AW Senior, T Green
bioRxiv, 2021
62021
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
62016
Protein complex prediction with AlphaFold-Multimer. 2021
R Evans, M O’neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ...
https://www. biorxiv. org/content/10.1101/2021.10 4, v2, 0
6
Protein complex prediction with AlphaFold-Multimer. bioRxiv. 2021: 2021.10. 04.463034
R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ...
View Article, 0
6
Protein complex prediction with AlphaFold-Multimer. bioRxiv
R Evans, M O’Neill, A Pritze, N Antropova, T Senior, A Žídek, R Bates, ...
Preprint posted Oct 4, 2021, 2022
42022
Use of Deep Learning in the Classification of Benign Lesions, Lumina! A Cancers, and Other Molecular Cancer Subtypes in Breast Magnetic Resonance Imaging
H Whitney, N Antropova, M Giger
MEDICAL PHYSICS 45 (6), E175-E175, 2018
22018
A Deep Learning Approach for Characterizing Major Galaxy Mergers
S Koppula, V Bapst, M Huertas-Company, S Blackwell, ...
arXiv preprint arXiv:2102.05182, 2021
12021
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