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The work discusses the use of machine learning algorithms for anomalydetection in medical image analysis and how the performance of these algorithmsdepends on the number of annotators and the quality of labels. To address theissue of subjectivity in labeling with a single annotator, we introduce asimple and effective approach that aggregates annotations from multipleannotators with varying levels of expertise. We then aim to improve theefficiency of predictive models in abnormal detection tasks by estimatinghidden labels from multiple annotations and using a re-weighted loss functionto improve detection performance. Our method is evaluated on a real-worldmedical imaging dataset and outperforms relevant baselines that do not considerdisagreements among annotators.

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation