主动标签清洗用于透射电子显微镜图像中电子致密沉积物的可靠检测
Active Label Cleaning for Reliable Detection of Electron Dense Deposits in Transmission Electron Microscopy Images
AI总结：
本文提出主动标签清洗方法，通过主动学习和专家重新标注，提升透射电子显微镜图像中电子致密沉积物检测的准确性和效率，降低标注成本。
摘要：
自动检测肾小球疾病中的电子致密沉积物（EDD）受到高质量标注数据稀缺的阻碍。尽管众包减少了标注成本，但引入了标签噪声。我们提出了一种主动标签清洗方法，以高效地去除众包数据集中的噪声。我们的方法利用主动学习选择最有价值的噪声样本进行专家重新标注，构建高精度清洗模型。一个标签选择模块利用众包标签与模型预测之间的差异，用于样本选择和实例级噪声分级。实验表明，我们的方法在私人数据集上实现了67.18%的AP50，比在噪声标签上训练提高了18.83%。这种性能达到了完全专家标注性能的95.79%，同时将标注成本降低了73.30%。该方法为在有限专家资源下开发可靠的医疗AI提供了一种实用且经济有效的解决方案。
英文摘要：
Automated detection of electron dense deposits (EDD) in glomerular disease is hindered by the scarcity of high-quality labeled data. While crowdsourcing reduces annotation cost, it introduces label noise. We propose an active label cleaning method to efficiently denoise crowdsourced datasets. Our approach uses active learning to select the most valuable noisy samples for expert re-annotation, building high-accuracy cleaning models. A Label Selection Module leverages discrepancies between crowdsourced labels and model predictions for both sample selection and instance-level noise grading. Experiments show our method achieves 67.18% AP\textsubscript{50} on a private dataset, an 18.83% improvement over training on noisy labels. This performance reaches 95.79% of that with full expert annotation while reducing annotation cost by 73.30%. The method provides a practical, cost-effective solution for developing reliable medical AI with limited expert resources.
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