论文标题
低剂量CT图像使用平行 - 键入网络
Low-Dose CT Image Denoising Using Parallel-Clone Networks
论文作者
论文摘要
深度神经网络具有改善低剂量计算机断层扫描（LDCT）的图像降解的巨大潜力。增加网络容量的流行方式包括添加更多层或重复序列中的模块化克隆模型。在这样的顺序体系结构中，嘈杂的输入图像和最终输出图像在训练模型中通常仅一次使用，这限制了整体学习绩效。在本文中，我们提出了一种使用模块化网络模型的平行 - 线神经网络方法，并利用了并行输入，平行输出损失和克隆 - 托克隆特征传输的好处。与常规模型相比，提出的模型保持相似或更少的未知网络权重，但可以大大加速学习过程。使用Mayo LDCT数据集评估了该方法，并将其与现有的深度学习模型进行了比较。结果表明，使用并联输入，平行输出损失和克隆到克隆特征转移都可以促进深度学习的加速收敛，并导致改善测试的图像质量。平行连锁网络已证明了LDCT图像Denoisising有希望的。
Deep neural networks have a great potential to improve image denoising in low-dose computed tomography (LDCT). Popular ways to increase the network capacity include adding more layers or repeating a modularized clone model in a sequence. In such sequential architectures, the noisy input image and end output image are commonly used only once in the training model, which however limits the overall learning performance. In this paper, we propose a parallel-clone neural network method that utilizes a modularized network model and exploits the benefit of parallel input, parallel-output loss, and clone-toclone feature transfer. The proposed model keeps a similar or less number of unknown network weights as compared to conventional models but can accelerate the learning process significantly. The method was evaluated using the Mayo LDCT dataset and compared with existing deep learning models. The results show that the use of parallel input, parallel-output loss, and clone-to-clone feature transfer all can contribute to an accelerated convergence of deep learning and lead to improved image quality in testing. The parallel-clone network has been demonstrated promising for LDCT image denoising.
