Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, previous work applied domain knowledge from human experts to qualitatively assess classification difficulty of medical images to guide curriculum learning, which requires extra annotation efforts, relies on subjective human experience, and may introduce bias. In this work, we propose a new automated curriculum learning technique using the variance of gradients (VoG) to compute an objective difficulty measure of samples and evaluated its effects on elbow fracture classification from X-ray images. Specifically, we used VoG as a metric to rank each sample in terms of the classification difficulty, where high VoG scores indicate more difficult cases for classification, to guide the curriculum training process We compared the proposed technique to a baseline (without curriculum learning), a previous method that used human annotations on classification difficulty, and anti-curriculum learning. Our experiment results showed comparable and higher performance for the binary and multi-class bone fracture classification tasks.

翻译：课程学习是一种按照从简单到困难的有意义顺序训练模型的学习方法。其中一个关键点在于设计自动且客观的样本难度度量方式。在医学领域，以往的研究依赖人类专家的领域知识，对医学图像的分类难度进行定性评估以指导课程学习，这需要额外的标注工作，依赖主观的人类经验，并可能引入偏差。本研究提出了一种新的自动课程学习技术，利用梯度方差（VoG）计算样本的目标难度度量，并在X射线图像中的肘部骨折分类任务上评估了其效果。具体而言，我们使用VoG作为指标，根据样本的分类难度进行排序（高VoG分数表示更困难的分类案例），从而指导课程训练过程。我们将所提出的技术与基线方法（无课程学习）、以往依赖人类标注分类难度的方法以及反课程学习方法进行了比较。实验结果表明，在二分类和多分类骨折分类任务中，该技术达到了可比较甚至更高的性能。