|A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction|
A Yala, C Lehman, T Schuster, T Portnoi, R Barzilay
|Improving information extraction by acquiring external evidence with reinforcement learning|
K Narasimhan, A Yala, R Barzilay
arXiv preprint arXiv:1603.07954, 2016
|Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation|
Constance Lehman, Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl ...
|Using machine learning to parse breast pathology reports|
A Yala, R Barzilay, L Salama, M Griffin, G Sollender, A Bardia, C Lehman, ...
Breast cancer research and treatment 161 (2), 203-211, 2017
|A deep learning model to triage screening mammograms: a simulation study|
A Yala, T Schuster, R Miles, R Barzilay, C Lehman
Radiology 293 (1), 38-46, 2019
|Machine learning to parse breast pathology reports in Chinese|
R Tang, L Ouyang, C Li, Y He, M Griffin, A Taghian, B Smith, A Yala, ...
Breast cancer research and treatment 169 (2), 243-250, 2018
|Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions|
F Acevedo, VD Armengol, Z Deng, R Tang, SB Coopey, D Braun, A Yala, ...
Breast Cancer Research and Treatment 173 (1), 201-207, 2019
|Deep learning model to assess cancer risk on the basis of a breast MR image alone|
T Portnoi, A Yala, T Schuster, R Barzilay, B Dontchos, L Lamb, C Lehman
American Journal of Roentgenology 213 (1), 227-233, 2019
|Incidental breast carcinoma: incidence, management, and outcomes in 4804 bilateral reduction mammoplasties|
R Tang, F Acevedo, C Lanahan, SB Coopey, A Yala, R Barzilay, C Li, ...
Breast cancer research and treatment 177 (3), 741-748, 2019
|External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice|
BN Dontchos, A Yala, R Barzilay, J Xiang, CD Lehman
Academic Radiology, 2020
|Do Neural Information Extraction Algorithms Generalize Across Institutions?|
E Santus, C Li, A Yala, D Peck, R Soomro, N Faridi, I Mamshad, R Tang, ...
JCO clinical cancer informatics 3, 1-8, 2019
|ml-RECIST: Machine learning to estimate RECIST in patients with NSCLC treated with PD-(L) 1 blockade.|
KC Arbour, L Anh Tuan, H Rizvi, A Yala, MD Hellmann, R Barzilay
Journal of Clinical Oncology 37 (15_suppl), 9052-9052, 2019
|Atypical ductal hyperplasia in men with gynecomastia: what is their breast cancer risk?|
SB Coopey, K Kartal, C Li, A Yala, R Barzilay, HR Faulkner, TA King, ...
Breast Cancer Research and Treatment 175 (1), 1-4, 2019
|Role of tumor microenvironment, as assessed by breast MRI background parenchymal enhancement (BPE), in modulating response to neoadjuvant chemotherapy in young women with …|
L Spring, G Rutledge, A Yala, S Haddad, M Specht, B Moy, R Barzilay, ...
CANCER RESEARCH 77, 2017
|Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports|
E Santus, T Schuster, AM Tahmasebi, C Li, A Yala, CR Lanahan, ...
JCO Clinical Cancer Informatics 4, 865-874, 2020
|Deep learning to estimate RECIST in patients with NSCLC treated with PD-1 blockade|
KC Arbour, AT Luu, J Luo, H Rizvi, AJ Plodkowski, M Sakhi, KB Huang, ...
Cancer discovery 11 (1), 59-67, 2020
|Incidental atypical hyperplasia/LCIS in mammoplasty specimens and subsequent risk of breast cancer.|
F Acevedo, VD Armengol, Z Deng, R Tang, S Coopey, E Mazzola, ...
Journal of Clinical Oncology 37 (15_suppl), 1561-1561, 2019