论文标题
使用设置转移功能在基于深度学习的定量超声中校准数据不匹配
Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions
论文作者
论文摘要
当培训和测试数据之间存在数据不匹配时，深度学习（DL）可能会失败。由于其依赖运营商的性质，与采集相关的数据不匹配是由不同的扫描仪设置引起的，因此可以在超声成像中发生。因此，减轻此类数据不匹配的影响对于更广泛的DL供电超声成像的临床采用至关重要。为了减轻效果，理想情况下，我们需要在每个扫描仪环境下收集大型训练集。但是，获得此类训练集很昂贵。另一种方法可能是在成像设置的一部分中进行培训，这使数据生成更便宜。但是，仍然会有概括问题。作为一种廉价且可推广的替代方法，我们建议在单个设置上收集大型训练集，并在每个扫描仪设置处收集一个小的校准集。然后，校准集将使用信号和系统透视图来校准数据不匹配。我们测试了提出的解决方案，以对两个幻象进行分类。为了研究所提出的解决方案的普遍性，我们校准了三种类型的数据不匹配：脉冲频率，焦点和输出功率不匹配。为了校准设置不匹配，我们计算了设置传输函数。没有校准的CNN训练，导致脉冲频率，聚焦和输出功率不匹配的平均分类精度分别为55.3％，64.4％和70.3％。通过使用允许训练和测试域匹配的设置传输功能，我们的平均精度分别为95.3％，92.99％和99.32％。因此，扫描仪设置之间的设置传输功能的合并可以提供一种经济的手段，将DL模型推广到扫描仪设置未由操作员确定的特定分类任务。
Deep learning (DL) can fail when there are data mismatches between training and testing data. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imaging. Therefore, mitigating effects of such data mismatches is essential for wider clinical adoption of DL powered ultrasound imaging. To mitigate the effects, ideally we need to collect a large training set at each scanner setting. However, acquiring such training sets is expensive. Another approach could be training on a subset of imaging settings, which makes the data generation less expensive. However, there will still be generalization issues. As an alternative approach that is inexpensive and generalizable, we propose to collect a large training set at a single setting and a small calibration set at each scanner setting. Then, the calibration set will be used to calibrate data mismatches by using a signals and systems perspective. We tested the proposed solution to classify two phantoms. To investigate generalizability of the proposed solution, we calibrated three types of data mismatches: pulse frequency, focus and output power mismatches. To calibrate the setting mismatches, we calculated the setting transfer functions. The CNN trained with no calibration resulted in mean classification accuracies of 55.3%, 64.4% and 70.3% for pulse frequency, focus and output power mismatches, respectively. By using the setting transfer functions, which allowed a matching of the training and testing domains, we obtained mean accuracies of 95.3%, 92.99% and 99.32%, respectively. Therefore, the incorporation of the setting transfer functions between scanner settings can provide an economical means of generalizing a DL model for specific classification tasks where scanner settings are not fixed by the operator.
