Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected for clinical purposes often have such markings embedded in them. In dermatology, known examples include drawings or rulers that are overrepresented in images of malignant lesions. In this paper, we encounter text and calipers placed on the images found in national databases containing fetal screening ultrasound scans, which correlate with standard planes to be predicted. In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.

翻译：医学图像中的文字或标记混淆信息可能会严重影响诊断深度学习算法的训练。然而，为临床目的收集的数据通常具有这样的标记。在皮肤病学中，已知包括在恶性病变图像中过度出现的绘图或标尺。本文中，我们遇到了嵌入在国家数据库中包含胎儿筛查超声扫描的图像中的文本和卡尺，这些与要预测的标准平面相关。为了利用这些数据库中可用的大量数据，我们开发并验证了一系列方法，以最小化嵌入在超声设计的深度学习算法上的文本和卡尺对标准平面分类的混淆效应。