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.

本研究探讨了在医学影像分析中利用机器学习算法进行异常检测的方法，重点讨论了算法的性能如何取决于标注者的数量和标签的质量。我们提出了一种简单有效的方法，通过聚合不同水平标注者的标注来解决单个标注者主观性标注的问题。通过估计多个标注的隐藏标签并使用重新加权的损失函数来提高检测性能，我们旨在提高异常检测任务中预测模型的效率。本研究在一个真实的医学影像数据集上进行了评估，并超越了不考虑标注者间差异的相关基线。

通过多位专家注释员提高医学图像分析中的物体检测：一项实证研究