自动检测乳腺摄影中金属伪影，发现其严重影响诊断模型性能。
Radio-opaque artefacts in digital mammography: automatic detection and analysis of downstream effects
- 基于2.2万张图像构建多标签伪影检测器，识别5类常见金属伪影
- 伪影使模型分类阈值偏移，输出分布畸变，影响密度与癌症筛查准确率
- 开源标注数据、代码与预测结果，助力后续医学影像研究
本研究分析皮肤标记、乳房植入物、起搏器等放射不透明伪影对数字乳腺摄影分类模型的影响。在公开的EMBED数据集上人工标注22,012张乳腺钼靶图像后，开发了一种鲁棒的多标签伪影检测器，可识别五类伪影：圆形和三角形皮肤标记、乳房植入物、支撑装置及点压结构。在乳腺密度评估与癌症筛查两个临床任务上的实验表明，这些伪影会显著影响模型性能，改变分类阈值，并扭曲输出分布。研究强调了精准自动伪影检测对构建可靠、鲁棒分类模型的重要性。为促进后续研究，本工作公开了标注数据、代码及模型预测结果。
原文摘要 · Abstract (English)
This study investigates the effects of radio-opaque artefacts, such as skin markers, breast implants, and pacemakers, on mammography classification models. After manually annotating 22,012 mammograms from the publicly available EMBED dataset, a robust multi-label artefact detector was developed to identify five distinct artefact types (circular and triangular skin markers, breast implants, support devices and spot compression structures). Subsequent experiments on two clinically relevant tasks $-$ breast density assessment and cancer screening $-$ revealed that these artefacts can significantly affect model performance, alter classification thresholds, and distort output distributions. These findings underscore the importance of accurate automatic artefact detection for developing reliable and robust classification models in digital mammography. To facilitate future research our annotations, code, and model predictions are made publicly available.
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