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Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks
With the advent of Deep Learning (DL) techniques, especially Generative ...
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STAN-CT: Standardizing CT Image using Generative Adversarial Network
Computed tomography (CT) plays an important role in lung malignancy diag...
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Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks
Deep learning is a popular and powerful tool in computed tomography (CT)...
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Physics-Based Generative Adversarial Models for Image Restoration and Beyond
We present an algorithm to directly solve numerous image restoration pro...
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CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Automated lesion segmentation from computed tomography (CT) is an import...
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Generative Adversarial Networks for Extreme Learned Image Compression
We propose a framework for extreme learned image compression based on Ge...
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Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks
Microscopic images from different modality can provide more complete exp...
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Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.
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