We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging and annotations are noisy, we design a robust coarse-to-fine model that ranks images based on their semantic image quality and endow our predicted rankings with an uncertainty estimate. To annotate rankings on training data, we design an efficient ranking annotation scheme based on the merge sort algorithm. Finally, we compare our ranking algorithm to a number of state-of-the-art ranking algorithms on a challenging fetal ultrasound quality assessment task, showing the superior performance of our method on the majority of rank correlation metrics.

翻译：我们引入了语义图像质量的概念，以应对图像质量依赖于语义要求的应用场景。针对胎儿超声中排序困难且标注存在噪声的问题，我们设计了一种鲁棒的由粗到细模型，该模型根据图像的语义质量进行排序，并为预测的排序结果赋予不确定性估计。为了对训练数据进行排序标注，我们基于归并排序算法设计了一种高效的排序标注方案。最后，我们将提出的排序算法与多种先进排序算法在具有挑战性的胎儿超声质量评估任务中进行了比较，结果表明我们的方法在大多数排序相关性指标上具有更优性能。