User profiles for M.A. Mazurowski
 | Maciej A. MazurowskiAssociate Professor of Radiology, Computer Science, Electrical & Comp. Eng., and Biostat.& … Verified email at duke.edu Cited by 9069 |
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep‐…
units are used to extract patterns from data in order to solve complex problems. Deep‐…
A systematic study of the class imbalance problem in convolutional neural networks
In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used methods …
performance of convolutional neural networks (CNNs) and compare frequently used methods …
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
This study investigates the effect of class imbalance in training data when developing neural
network classifiers for computer-aided medical diagnosis. The investigation is performed in …
network classifiers for computer-aided medical diagnosis. The investigation is performed in …
Segment anything model for medical image analysis: an experimental study
Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation model …
limited availability of data annotations. Segment Anything Model (SAM) is a foundation model …
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors
Purpose To review features used in MRI radiomics of breast cancer and study the inter‐reader
stability of the features. Methods We implemented 529 algorithmic features that can be …
stability of the features. Methods We implemented 529 algorithmic features that can be …
Breast cancer radiogenomics: current status and future directions
LJ Grimm, MA Mazurowski - Academic Radiology, 2020 - Elsevier
Radiogenomics is an area of research that aims to identify associations between imaging
phenotypes (“radio-”) and tumor genome (“-genomics”). Breast cancer radiogenomics …
phenotypes (“radio-”) and tumor genome (“-genomics”). Breast cancer radiogenomics …
Radiogenomics: what it is and why it is important
MA Mazurowski - Journal of the American College of Radiology, 2015 - Elsevier
In recent years, a new direction in cancer research has emerged that focuses on the
relationship between imaging phenotypes and genomics. This direction is referred to as …
relationship between imaging phenotypes and genomics. This direction is referred to as …
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are
associated with shape features. In this study, we propose a fully automatic way to quantify …
associated with shape features. In this study, we propose a fully automatic way to quantify …
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging
Purpose To investigate associations between breast cancer molecular subtype and
semiautomatically extracted magnetic resonance (MR) imaging features. Materials and Methods …
semiautomatically extracted magnetic resonance (MR) imaging features. Materials and Methods …
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing
Background and purpose Convolutional neural networks ( CNN s) are commonly used for
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …