论文标题
大脑MRI图像的任意刻度超分辨率
Arbitrary Scale Super-Resolution for Brain MRI Images
论文作者
论文摘要
最近对医学图像的超分辨率尝试使用了深度学习技术，例如生成对抗网络（GAN）来实现感知现实的单图像超分辨率。然而，他们无法概括到不同的规模因素受到限制。这涉及高存储和能源成本，因为每个整数量表因子都涉及一个单独的神经网络。最近的一篇论文提出了一种新型的元学习技术，该技术使用权重预测网络仅使用单个神经网络来实现任意量表因子的超分辨率。在本文中，我们提出了一个新的网络，将该技术与基于GAN的最先进的建筑Srgan结合起来，以实现任意规模，高保真的医疗图像超级分辨率。通过使用该网络对来自多模式脑肿瘤分割挑战（BRATS）数据集的图像进行任意刻度：我们证明，它能够在SSIM分数上超过传统的传统插值方法，而在SSIM分数上最多可超过20美元$ \％$，而在大脑MRI图像上保留了普遍性。我们表明，跨量表的性能不会受到损害，并且能够通过其他最先进的方法（例如EDSR）获得竞争成果，同时比它们小五十倍。结合效率，性能和普遍性，这可以希望成为解决医疗图像超级分辨率的新基础。 在此处查看WebApp：https：//metasrgan.herokuapp.com/在此处查看GitHub教程：https：//github.com/pancakewaffles/metasrgan-tutorial
Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20$\%$ on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images. Check out the webapp here: https://metasrgan.herokuapp.com/ Check out the github tutorial here: https://github.com/pancakewaffles/metasrgan-tutorial
