The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.

翻译：本文探讨了机器学习算法在医学图像分析中用于异常检测的问题，并阐述了这些算法的性能如何依赖于标注人员的数量及标签质量。针对单一标注人员带来的主观性问题，我们提出了一种简单有效的方法，该方法能够聚合具有不同专业水平的多个标注人员的标注结果。进而，我们旨在通过从多个标注中估计隐藏标签，并利用重加权损失函数提升检测性能，从而提高异常检测任务中预测模型的效率。我们的方法在真实医学影像数据集上进行了评估，其性能优于未考虑标注人员间不一致性的相关基线方法。