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      Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

      , , ,
      Academic Radiology
      Elsevier BV

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          ImageNet classification with deep convolutional neural networks

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            Brain tumor segmentation with Deep Neural Networks

            In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.
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              Generative adversarial nets

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                Author and article information

                Contributors
                Journal
                Academic Radiology
                Academic Radiology
                Elsevier BV
                10766332
                August 2020
                August 2020
                : 27
                : 8
                : 1175-1185
                Article
                10.1016/j.acra.2019.12.024
                32035758
                52d87f03-157b-405f-a180-f26835ffaf2c
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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