To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification. We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies. We apply both quantitative and qualitative analyses, using standard metrics (F1-score, sensitivity, and precision), Class Activation Mapping (CAM), and t-Distributed Stochastic Neighbor Embedding (t-SNE). When dealing with high inter-class variability classification tasks, contrastive learning does not bring a significant advantage; whereas it results to be relevant for low inter-class variability classification, specifically when initialized with ImageNet weights. Contrastive learning approaches are typically used when a large number of unlabeled data is available, which is not representative of US datasets. We proved that SimClr either as pre-training with backbone initialized via ImageNet weights or used in an end-to-end dual-task may impact positively the performance over standard transfer learning approaches, under a scenario in which the dataset is small and characterized by low inter-class variability.

中文翻译：

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对比学习在胎儿超声成像标准平面分类中的应用

研究对比学习（特别是 SimClr）在减少胎儿标准平面识别对大型带注释超声 (US) 图像数据集的需求方面的有效性。我们探讨了 SimClr 在类间变异性低和高的情况下的优势，同时考虑分类性能如何根据所使用的不同数量的标签而变化。这种评估是通过不同的训练策略利用对比学习来进行的。我们使用标准指标（F1 分数、灵敏度和精度）、类激活映射 (CAM) 和 t 分布随机邻域嵌入 (t-SNE) 进行定量和定性分析。在处理类间变异性较高的分类任务时，对比学习并没有带来显着的优势；而它的结果与低类间变异性分类相关，特别是在使用 ImageNet 权重初始化时。当有大量未标记数据可用时，通常使用对比学习方法，这不能代表美国数据集。我们证明，在数据集较小且特征为类间变异性低。