用众包方式标注镰状细胞病血涂片图像，准确度接近专家水平。
Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users
- 通过亚马逊众包平台招募非专家标注血涂片图像。
- 达成共识后错误率极低，与专家标注结果高度一致。
- 适合用于构建大规模标注数据集，支持自动化诊断模型训练。
本文提出一种基于人力的计算方法，用于分析镰状细胞病（SCD）患者的外周血涂片（PBS）图像。我们利用亚马逊机械土耳其人（Mechanical Turk）微任务市场，众包完成PBS图像的标注工作，并使用专家标注的红细胞数据集IDB评估所提方法的准确性和可靠性。结果显示，当众包工作者达成稳健共识时，错误概率极低，与专家分析结果高度一致。这表明该方法可用于标注大量PBS图像数据，进而支持自动化诊断算法的训练。未来工作将探索该方法与自动分析技术的融合，以开发更精准可靠的SCD诊断手段。
原文摘要 · Abstract (English)
In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD
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