论文标题
针对无监督的低剂量CT DeNoising的贴剂深度度量学习
Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising
论文作者
论文摘要
低剂量和高剂量CT图像的采集条件通常不同，因此CT数字的变化经常发生。因此，学习目标图像分布的无监督的基于深度学习的方法通常会引入CT数字扭曲，并在诊断性能中造成不利影响。为了解决这个问题，我们在这里提出了一种新颖的无监督学习方法，用于使用贴片深度度量学习，以低水平CT重建。关键思想是通过拉动具有相同解剖结构的图像贴片的正面对来学习嵌入空间，并推动具有相同噪声水平的负对。因此，该网络经过训练以抑制噪声水平，同时即使在图像翻译后仍保留原始的全局CT数字分布。实验结果证实，我们的深度度量学习在产生没有CT数字的高质量DeNo的图像中起着至关重要的作用。
The acquisition conditions for low-dose and high-dose CT images are usually different, so that the shifts in the CT numbers often occur. Accordingly, unsupervised deep learning-based approaches, which learn the target image distribution, often introduce CT number distortions and result in detrimental effects in diagnostic performance. To address this, here we propose a novel unsupervised learning approach for lowdose CT reconstruction using patch-wise deep metric learning. The key idea is to learn embedding space by pulling the positive pairs of image patches which shares the same anatomical structure, and pushing the negative pairs which have same noise level each other. Thereby, the network is trained to suppress the noise level, while retaining the original global CT number distributions even after the image translation. Experimental results confirm that our deep metric learning plays a critical role in producing high quality denoised images without CT number shift.
