论文标题
深度学习CT重建中完全连接的层权重的可视化
Visualization of fully connected layer weights in deep learning CT reconstruction
论文作者
论文摘要
最近，将深度学习技术用于重建计算机断层扫描（CT）图像已成为一个热门研究主题，包括正弦域方法，图像域方法和正弦图域来图像域方法。所有这些方法都取得了有利的结果。在本文中，我们研究了在曲构域中使用的完全连接层的重要功能，用于图像域方法。首先，我们提出一个简单的域映射神经网络。然后，我们分析这些网络完全连接的层的作用，并视觉分析完全连接的层的权重。最后，通过可视化完全连接的层的权重，我们发现完全连接的层的主要作用是在CT重建中实现背部投影函数。这一发现对于使用深度学习技术来重建计算机断层扫描（CT）图像具有重要意义。例如，由于完全连接的层权重需要消耗大量的内存资源，因此可以通过使用分析算法来避免资源职业来实现反向预测函数，这些算法可以嵌入整个网络中。
Recently, the use of deep learning techniques to reconstruct computed tomography (CT) images has become a hot research topic, including sinogram domain methods, image domain methods and sinogram domain to image domain methods. All these methods have achieved favorable results. In this article, we have studied the important functions of fully connected layers used in the sinogram domain to image domain approach. First, we present a simple domain mapping neural networks. Then, we analyze the role of the fully connected layers of these networks and visually analyze the weights of the fully connected layers. Finally, by visualizing the weights of the fully connected layer, we found that the main role of the fully connected layer is to implement the back projection function in CT reconstruction. This finding has important implications for the use of deep learning techniques to reconstruct computed tomography (CT) images. For example, since fully connected layer weights need to consume huge memory resources, the back-projection function can be implemented by using analytical algorithms to avoid resource occupation, which can be embedded in the entire network.
