论文标题
部分可观测时空混沌系统的无模型预测
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging
论文作者
论文摘要
最近，使用神经网络（NNS）加速了自适应波束进行超声（US）成像的努力。但是，大多数这些努力都是基于静态模型的，即，他们接受了单个自适应波束形成方法（例如，最小差异无扭曲响应（MVDR））的培训，前提是它们会导致最佳图像质量。此外，只有在获取了一系列消耗了数字千兆字节（GB）存储空间的数据之后才能启动此类NN的培训。在这项研究中，首次在NNS的背景下首次描述了用于波束形成的主动学习框架。用户选择的最好的质量图像是拟议技术的基础真相，该技术与数据acqusition同时训练NN。平均而言，主动学习方法需要0.5秒才能完成一次训练的单个迭代。
In the recent past, there have been many efforts to accelerate adaptive beamforming for ultrasound (US) imaging using neural networks (NNs). However, most of these efforts are based on static models, i.e., they are trained to learn a single adaptive beamforming approach (e.g., minimum variance distortionless response (MVDR)) assuming that they result in the best image quality. Moreover, the training of such NNs is initiated only after acquiring a large set of data that consumes several gigabytes (GBs) of storage space. In this study, an active learning framework for beamforming is described for the first time in the context of NNs. The best quality image chosen by the user serves as the ground truth for the proposed technique, which trains the NN concurrently with data acqusition. On average, the active learning approach takes 0.5 seconds to complete a single iteration of training.
