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Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Cloud based medical image analysis has become popular recently due to th...
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Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image ana...
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Real-Time Impulse Noise Removal from MR Images for Radiosurgery Applications
In the recent years image processing techniques are used as a tool to im...
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Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Although deep neural networks have been a dominant method for many 2D vi...
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Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images....
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Adaptive Real-Time Removal of Impulse Noise in Medical Images
Noise is an important factor that degrades the quality of medical images...
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Indexing Medical Images based on Collaborative Experts Reports
A patient is often willing to quickly get, from his physician, reliable ...
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Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc. All these applications are proposed to automatically analyze medical images beforehand, which brings more information to radiologists during clinical assessment for accuracy improvement. Recently, many medical denoising methods had shown their significant artifact reduction result and noise removal both quantitatively and qualitatively. However, those existing methods are developed around human-vision, i.e., they are designed to minimize the noise effect that can be perceived by human eyes. In this paper, we introduce an application-guided denoising framework, which focuses on denoising for the following neural networks. In our experiments, we apply the proposed framework to different datasets, models, and use cases. Experimental results show that our proposed framework can achieve a better result than human-vision denoising network.
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