M
Maciej A. Mazurowski
Researcher at Duke University
Publications - 161
Citations - 6687
Maciej A. Mazurowski is an academic researcher from Duke University. The author has contributed to research in topics: Breast cancer & Deep learning. The author has an hindex of 30, co-authored 145 publications receiving 4462 citations. Previous affiliations of Maciej A. Mazurowski include Wrocław University of Technology & University of Louisville.
Papers
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Journal ArticleDOI
A systematic study of the class imbalance problem in convolutional neural networks
TL;DR: The effect of class imbalance on classification performance is detrimental; the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; and thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
Journal ArticleDOI
2008 Special Issue: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
Maciej A. Mazurowski,Piotr A. Habas,Jacek M. Zurada,Joseph Y. Lo,Jay A. Baker,Georgia D. Tourassi +5 more
TL;DR: The results show that classifier performance deteriorates with even modest class imbalance in the training data and it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features.
Proceedings Article
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
Maciej A. Mazurowski,Piotr A. Habas,Jacek M. Zurada,Joseph Y. Lo,Jay A. Baker,Georgia D. Tourassi +5 more
TL;DR: In this paper, two methods of neural network training are explored: classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria for computer-aided medical diagnosis.
Journal ArticleDOI
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.
TL;DR: 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 as mentioned in this paper, and it has shown promising performance in a variety of sophisticated tasks, especially those related to images.
Journal ArticleDOI
Radiogenomics: what it is and why it is important.
TL;DR: It is argued that the significance of radiogenomics is largely related to practical limitations of currently available data that often lack complete characterization of the patients and poor integration of individual datasets.