The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however, obtaining such datasets is particularly challenging since annotations must be provided by specialized physicians, which severely limits the pool of annotators. Furthermore, class boundaries can often be ambiguous or difficult to define which further complicates machine learning-based classification. In this paper, we want to address this problem and introduce a framework for mislabel detection in medical datasets. This is validated on the two largest, publicly available datasets for Video Capsule Endoscopy, an important imaging procedure for examining the gastrointestinal tract based on a video stream of lowresolution images. In addition, potentially mislabeled samples identified by our pipeline were reviewed and re-annotated by three experienced gastroenterologists. Our results show that the proposed framework successfully detects incorrectly labeled data and results in an improved anomaly detection performance after cleaning the datasets compared to current baselines.

翻译：深度神经网络的分类性能在很大程度上依赖于获取大规模且标注准确的数据集。然而，在医学影像领域，获取此类数据集尤为困难，因为标注必须由专业医师提供，这严重限制了标注人员的规模。此外，类别边界常常模糊不清或难以界定，这进一步增加了基于机器学习的分类任务的复杂性。本文旨在解决这一问题，提出一种用于医学数据集中误标检测的框架。该框架在两个最大的公开视频胶囊内窥镜数据集上进行了验证——视频胶囊内窥镜是一种基于低分辨率图像视频流检查胃肠道的重要成像技术。此外，我们通过三位经验丰富的胃肠病学专家对算法流程识别出的潜在误标样本进行了复核与重新标注。实验结果表明，所提出的框架能有效检测错误标注的数据，且在清洗数据集后，相较于现有基线方法，异常检测性能得到了提升。