Maciej A. Mazurowski

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Ashirbani SahaAssistant Professor, Department of Oncology, McMaster UniversityDirección de correo verificada de mcmaster.ca
Joseph Y. LoProfessor of Radiology, Biomed. Engineering, Elec. Engineering, Med PhysicsDirección de correo verificada de duke.edu
Lars GrimmDuke University Medical CenterDirección de correo verificada de duke.edu
Georgia TourassiOak Ridge National LabDirección de correo verificada de ornl.gov
Mateusz BudaDuke UniversityDirección de correo verificada de duke.edu
Jacek M. ZuradaProfessor of Electrical and Computer Engineering, University of LouisvilleDirección de correo verificada de louisville.edu
Hanxue GuDuke UniversityDirección de correo verificada de duke.edu
Michael HarowiczCardiothoracic Radiology Fellow, Duke UniversityDirección de correo verificada de jhmi.edu
Nicholas KonzPhD Candidate, Duke UniversityDirección de correo verificada de duke.edu
Jeffrey R. MarksDuke UniversityDirección de correo verificada de mc.duke.edu
Haoyu DongDuke UniversityDirección de correo verificada de duke.edu
Jing ZhangLamar UniversityDirección de correo verificada de lamar.edu
Jay A BakerProfessor of Radiology, Duke University Medical CenterDirección de correo verificada de duke.edu
Zhe Zhu(朱哲)Samsung Research AmericaDirección de correo verificada de samsung.com
Benjamin Wildman-TobrinerAssistant Professor of Radiology, Duke UniversityDirección de correo verificada de duke.edu
Andy (Jichen) YangPh.D. Candidate, Duke UniversityDirección de correo verificada de duke.edu
Mustafa BashirProfessor of Radiology, Professor in the Department of Medicine, Duke UniversityDirección de correo verificada de duke.edu
Jun ZhangTencentDirección de correo verificada de tencent.com
Piotr A. HabasAmazonDirección de correo verificada de uw.edu
Carlo C. MaleyBiodesign Institute, Arizona State UniversityDirección de correo verificada de asu.edu

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Maciej A. Mazurowski

Associate Professor of Radiology, Computer Science, Electrical & Comp. Eng., and Biostat.& Bioinf.

Dirección de correo verificada de duke.edu - Página principal

Machine Learning Deep learning Data Science Radiogenomics Medical Imaging


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
A systematic study of the class imbalance problem in convolutional neural networks M Buda, A Maki, MA Mazurowski Neural Networks 106, 249-259, 2018	2920	2018
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance MA Mazurowski, PA Habas, JM Zurada, JY Lo, JA Baker, GD Tourassi Neural Networks 21 (2-3), 427-436, 2008	1087	2008
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI MA Mazurowski, M Buda, A Saha, MR Bashir Journal of Magnetic Resonance Imaging 49 (4), 939-954, 2019	618	2019
Segment anything model for medical image analysis: an experimental study MA Mazurowski, H Dong, H Gu, J Yang, N Konz, Y Zhang Medical Image Analysis 89, 102918, 2023	474	2023
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm M Buda, A Saha, MA Mazurowski Computers in biology and medicine 109, 218-225, 2019	327	2019
Radiogenomics: What It Is and Why It Is Important MA Mazurowski Journal of the American College of Radiology 12 (8), 862-866, 2015	317	2015
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing EA AlBadawy, A Saha, MA Mazurowski Medical physics 45 (3), 1150-1158, 2018	266	2018
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging MA Mazurowski, J Zhang, LJ Grimm, SC Yoon, JI Silber Radiology 273 (2), 365-372, 2014	264	2014
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features A Saha, MR Harowicz, LJ Grimm, CE Kim, SV Ghate, R Walsh, ... British journal of cancer 119 (4), 508-516, 2018	261	2018
Management of thyroid nodules seen on US images: deep learning may match performance of radiologists M Buda, B Wildman-Tobriner, JK Hoang, D Thayer, FN Tessler, ... Radiology 292 (3), 695-701, 2019	197	2019
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set EH Cain, A Saha, MR Harowicz, JR Marks, PK Marcom, MA Mazurowski Breast cancer research and treatment 173, 455-463, 2019	196	2019
Hierarchical convolutional neural networks for segmentation of breast tumors in mri with application to radiogenomics J Zhang, A Saha, Z Zhu, MA Mazurowski IEEE transactions on medical imaging 38 (2), 435-447, 2019	183	2019
Deep Learning for identifying radiogenomic associations in breast cancer Z Zhu, E Albadawy, A Saha, J Zhang, MR Harowicz, MA Mazurowski Computers in biology and medicine 109, 85-90, 2019	164	2019
Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using … LJ Grimm, J Zhang, MA Mazurowski Journal of Magnetic Resonance Imaging 42 (4), 902-907, 2015	160	2015
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The … MA Mazurowski, K Clark, NM Czarnek, P Shamsesfandabadi, KB Peters, ... Journal of neuro-oncology 133, 27-35, 2017	143	2017
Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility B Wildman-Tobriner, M Buda, JK Hoang, WD Middleton, D Thayer, ... Radiology 292 (1), 112-119, 2019	128	2019
Imaging descriptors improve the predictive power of survival models for glioblastoma patients MA Mazurowski, A Desjardins, JM Malof Neuro-oncology 15 (10), 1389-1394, 2013	125	2013
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, ... JAMA network open 4 (8), e2119100-e2119100, 2021	106*	2021
Artificial intelligence may cause a significant disruption to the radiology workforce MA Mazurowski Journal of the American College of Radiology 16 (8), 1077-1082, 2019	95	2019
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists A Swiecicki, N Li, J O'Donnell, N Said, J Yang, RC Mather, WA Jiranek, ... Computers in biology and medicine 133, 104334, 2021	85	2021

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