While domain-specific data augmentation can be useful in training neural
networks for medical imaging tasks, such techniques have not been widely used
to date. Here, we test whether domain-specific data augmentation is useful for
medical imaging using a well-benchmarked task: view classification on fetal
ultrasound FETAL-125 and OB-125 datasets. We found that using a
context-preserving cut-paste strategy, we could create valid training data as
measured by performance of the resulting trained model on the benchmark test
dataset. When used in an online fashion, models trained on this data performed
similarly to those trained using traditional data augmentation (FETAL-125
F-score 85.33+/-0.24 vs 86.89+/-0.60, p-value 0.0139; OB-125 F-score
74.60+/-0.11 vs 72.43+/-0.62, p-value 0.0039). Furthermore, the ability to
perform augmentations during training time, as well as the ability to apply
chosen augmentations equally across data classes, are important considerations
in designing a bespoke data augmentation. Finally, we provide open-source code
to facilitate running bespoke data augmentations in an online fashion. Taken
together, this work expands the ability to design and apply domain-guided data
augmentations for medical imaging tasks.

本研究探讨了领域特定的数据增强对医学成像任务的作用，使用了胎儿超声 FETAL-125 和 OB-125 数据集，发现通过上下文保留的 cut-paste 策略，可以创建有效的训练数据，并通过基准测试数据集的表现来评估模型性能，结果表明，通过在线方式训练的模型在 FETAL-125 和 OB-125 数据集上的表现与使用传统数据增强训练的模型类似。最后提供开源代码，便于设计并应用领域指导的数据增强策略处理医学成像任务。