Search Results - "Drukker, K."

WE‐E‐134‐04: Computerized Detection of Breast Cancer On Automated Breast Ultrasound for Women with Dense Breasts by Drukker, K, Sennett, C, Giger, M

“…Purpose. Develop a computer‐aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound for women with…”
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Computerized lesion detection on breast ultrasound by Drukker, Karen, Giger, Maryellen L., Horsch, Karla, Kupinski, Matthew A., Vyborny, Carl J., Mendelson, Ellen B.

“…We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering,…”
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Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound by Drukker, K., Sennett, C.A., Giger, M.L.

“…The purpose of this research was to demonstrate the feasibility of a computerized auto-assessment method in which a computer-aided diagnosis (CAD x ) system…”
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TU‐E‐217BCD‐07: Pilot Study on Consistency in Size Metrics for a Multimodality PEM/MR Breast Imaging Approach by Sadinski, M, Giger, M, Drukker, K, Yamaguchi, K, Lan, L, Li, H

“…Purpose: In this study, we evaluated the degree of consistency between size metrics obtained from PEM and MRI to determine the intrinsic effectiveness of a…”
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WE‐D‐304A‐01: Three‐Class Classification for Binary Decision Making by He, X, Drukker, K, Brankov, J, Frey, E

“…Purpose: The goal of this work is to prove theoretically, and demonstrate in an example application, that 3‐class classification in a 2D decision space results…”
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WE‐D‐304A‐03: Quantitative Image Analysis for Prognosis in Newly Diagnosed Breast Cancer Patients with Sonographically Normal Appearing Lymph Nodes by Drukker, K, Sennett, C, Giger, M

“…Purpose: To investigate whether a computerized quantitative image analysis (QIA) system for diagnostic breast ultrasound can predict whether a breast cancer…”
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Computerized analysis of shadowing on breast ultrasound for improved lesion detection by Drukker, Karen, Giger, Maryellen L., Mendelson, Ellen B.

“…Sonography is being considered for the screening of women at high risk for breast cancer. We are developing computerized detection methods to aid in the…”
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SU‐GG‐I‐03: Self‐Assessment as a Strategy to Improve Performance of Computer‐Aided Diagnosis in Breast Ultrasound by Drukker, K, Sennett, C, Giger, M

“…Purpose: The aim of this work was to develop and apply a computerized auto‐assessment method in which a computer‐aided diagnosis (CADx) system itself provides…”
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TU-B-207B-01: Challenges in Radiomics and Big Data by Drukker, K.

“…The general learning objectives are to get a better understanding of • The pitfalls along the radiomics/radiogenomics (imaging genomics) pipeline • The crucial…”
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WE‐C‐332‐05: Preliminary Robustness Study of a Breast Ultrasound Computer‐Aided Diagnosis System Across Different Patient Populations by Gruszauskas, NP, Drukker, K, Giger, ML, Chang, RF, Moon, WK

“…Purpose: To determine the robustness of a breast ultrasound computer‐aided diagnosis system (CADx) when it is used across different patient populations. Method…”
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SU‐GG‐I‐149: Preliminary Study of the Accuracy of An Automated Segmentation Algorithm for Sonographic Breast Lesions Across Different Patient Populations by Gruszauskas, NP, Drukker, K, Giger, ML, Chang, RF, Moon, WK

“…Purpose: To determine the accuracy and variability of an automated sonographic breast lesion segmentation algorithm when it is used across different patient…”
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TH‐C‐I‐609‐10: Computerized Classification of Non‐Biopsied Lesions Seen On Breast Ultrasound by Drukker, K, Gruszauskas, N, Giger, ML

“…Purpose: To investigate the performance of a computerized lesion classification scheme in a realistic testing protocol resembling clinical practice…”
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MO-DE-207B-06: Computer-Aided Diagnosis of Breast Ultrasound Images Using Transfer Learning From Deep Convolutional Neural Networks by Huynh, B, Drukker, K, Giger, M

“…Purpose: To assess the performance of using transferred features from pre-trained deep convolutional networks (CNNs) in the task of classifying cancer in…”
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TU‐AB‐BRA‐07: Radiomics of Breast Cancer: A Robustness Study by Antropova, N, Giger, M, Li, H, Drukker, K, Lan, L

“…Purpose: Computer‐extracted image phenotypes (CEIPs) are being investigated as complimentary attributes in the characterization of breast cancer in radiomics/…”
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TU‐AB‐BRA‐08: Radiomics in the Analysis of Breast Cancer Heterogeneity On DCE‐MRI by Li, H, Lan, L, Drukker, K, Perou, C, Giger, M

“…Purpose: To investigate the heterogeneity of breast tumors in terms of variation in contrast enhancement as observed in computer‐extracted MRI‐based tumor…”
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Basics of Surface Hopping in Mixed Quantum/Classical Simulations by Drukker, Karen

“…This paper gives an overview of mixed quantum/classical simulation techniques based on the ideas of surface hopping (Tully, 1990). Basics such as the…”
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TU‐CD‐BRB‐06: Deciphering Genomic Underpinnings of Quantitative MRI‐Based Radiomic Phenotypes of Invasive Breast Carcinoma by Zhu, Y, Li, H, Guo, W, Drukker, K, Yang, S, Lan, L, Giger, M, Ji, Y

“…Purpose: Magnetic Resonance Imaging(MRI) has been routinely used for diagnosis and assessment of breast cancer. Despite its wide applications in clinical…”
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TU‐CD‐BRB‐03: Radiomics Investigation in the Distinction Between in Situ and Invasive Breast Cancers by Drukker, K, Schram, J, Burda, S, Li, H, Lan, L, Giger, M

“…Purpose: To determine the capability of machine learning and computer‐extracted image phenotypes to distinguish between ductal carcinoma in situ and invasive…”
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Robustness of computerized lesion detection and classification scheme across different breast US platforms by Drukker, Karen, Giger, Maryellen L, Metz, Charles E

“…To evaluate the performance of a computerized detection and diagnosis method with breast ultrasonographic (US) images obtained with US equipment from two…”
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A Novel Hybrid Linear/Nonlinear Classifier for Two-Class Classification: Theory, Algorithm, and Applications by Weijie Chen, Metz, C.E., Giger, M.L., Drukker, K.

“…Classifier design for a given classification task needs to take into consideration both the complexity of the classifier and the size of the dataset that is…”
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