论文标题
通过定量超声来进行组织表征的数据有效的深度学习策略：区域训练
A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training
论文作者
论文摘要
深度学习（DL）有动力的生物医学超声成像是一个新兴的研究领域，研究人员将DL算法的图像分析能力适应生物医学超声成像设置。更广泛采用DL动力生物医学超声成像的主要障碍是，在临床环境中获取大型和多样化的数据集很昂贵，这是成功实施DL的要求。因此，不断需要开发数据效率的DL技术，以将DL动力的生物医学超声成像变成现实。在这项工作中，我们制定了一种数据有效的深度学习培训策略，我们将其命名为\ textit {Zone Training}。在\ textIt {区域训练}中，我们建议将超声图像的完整视野划分为与衍射模式不同区域相关的多个区域，然后为每个区域训练单独的DL网络。 \ textit {区域培训}的主要优点是，它需要更少的培训数据才能获得高精度。在这项工作中，通过DL网络对三个不同的模拟幻影进行了分类。结果表明，与传统培训策略相比，\ textit {区域培训}需要减少2-5个训练数据的倍数，以达到类似的分类精度。
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy, which we named \textit{Zone Training}. In \textit{Zone Training}, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of \textit{Zone Training} is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that \textit{Zone Training} required a factor of 2-5 less training data to achieve similar classification accuracies compared to a conventional training strategy.
