|Introduction to statistical relational learning|
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
|ACR BI-RADS atlas: breast imaging reporting and data system; mammography, ultrasound, magnetic resonance imaging, follow-up and outcome monitoring, data dictionary|
American College of Radiology, CJ D'Orsi
ACR, American College of Radiology, 2013
|Acr bi-rads® mammography|
EA Sickles, CJ d’Orsi, LW Bassett, CM Appleton, WA Berg, ES Burnside
ACR BI-RADS® atlas, breast imaging reporting and data system 5, 2013, 2013
|Differentiating Benign from Malignant Solid Breast Masses with US Strain Imaging|
ES Burnside, TJ Hall, AM Sommer, GK Hesley, GA Sisney, WE Svensson, ...
Radiology 245 (2), 401-410, 2007
|MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays|
H Li, Y Zhu, ES Burnside, K Drukker, KA Hoadley, C Fan, SD Conzen, ...
Radiology 281 (2), 382-391, 2016
|The ACR BI-RADS® experience: learning from history|
ES Burnside, EA Sickles, LW Bassett, DL Rubin, CH Lee, DM Ikeda, ...
Journal of the American College of Radiology 6 (12), 851-860, 2009
|Use of Microcalcification Descriptors in BI-RADS 4th Edition to Stratify Risk of Malignancy|
ES Burnside, JE Ochsner, KJ Fowler, JP Fine, LR Salkowski, DL Rubin, ...
Radiology 242 (2), 388-395, 2007
|Toward Best Practices in Radiology Reporting|
CE Kahn Jr, CP Langlotz, ES Burnside, JA Carrino, DS Channin, ...
Radiology 252 (3), 852-856, 2009
|Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set|
H Li, Y Zhu, ES Burnside, E Huang, K Drukker, KA Hoadley, C Fan, ...
NPJ breast cancer 2 (1), 1-10, 2016
|Comparison of Logistic Regression and Artificial Neural Network Models in Breast Cancer Risk Estimation|
T Ayer, J Chhatwal, O Alagoz, CE Kahn Jr, RW Woods, ES Burnside
Radiographics 30 (1), 13-22, 2010
|Patient, faculty, and self-assessment of radiology resident performance: A 360-degree method of measuring professionalism and interpersonal/communication skills|
J Wood, J Collins, ES Burnside, MA Albanese, PA Propeck, F Kelcz, ...
Academic radiology 11 (8), 931-939, 2004
|Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration|
T Ayer, O Alagoz, J Chhatwal, JW Shavlik, CE Kahn Jr, ES Burnside
Cancer 116 (14), 3310-3321, 2010
|Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality|
D Munoz, AM Near, NT Van Ravesteyn, SJ Lee, CB Schechter, O Alagoz, ...
JNCI: Journal of the National Cancer Institute 106 (11), 2014
|Optimal breast biopsy decision-making based on mammographic features and demographic factors|
J Chhatwal, O Alagoz, ES Burnside
Operations research 58 (6), 1577-1591, 2010
|Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience|
ES Burnside, DL Rubin, JP Fine, RD Shachter, GA Sisney, WK Leung
Radiology 240 (3), 666-673, 2006
|Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data|
W Guo, H Li, Y Zhu, L Lan, S Yang, K Drukker, EA Morris, ES Burnside, ...
Journal of medical imaging 2 (4), 041007, 2015
|A Bayesian network for mammography.|
E Burnside, D Rubin, R Shachter
Proceedings of the AMIA Symposium, 106, 2000
|Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings|
ES Burnside, J Davis, J Chhatwal, O Alagoz, MJ Lindstrom, BM Geller, ...
Radiology 251 (3), 663-672, 2009
|Circulating serum xenoestrogens and mammographic breast density|
BL Sprague, A Trentham-Dietz, CJ Hedman, J Wang, JDC Hemming, ...
Breast Cancer Research 15 (3), 1-8, 2013
|An integrated approach to learning Bayesian networks of rules|
J Davis, E Burnside, I de Castro Dutra, D Page, VS Costa
European Conference on Machine Learning, 84-95, 2005