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      MultiMedia Modeling 

      Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            N4ITK: improved N3 bias correction.

            A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.
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              Image-to-Image Translation with Conditional Adversarial Networks

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                Book Chapter
                2018
                September 12 2018
                : 31-41
                10.1007/978-3-030-00536-8_4
                1882b6d3-6c3c-45ba-a9eb-cf5ad2932fb9
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