用于吉姆萨染色血涂片中东亚疟原虫检测的COCO格式实例级数据集
A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
AI总结：
针对吉姆萨染色血涂片疟原虫检测缺乏实例级注释数据集的问题，提出NIH疟疾数据集的COCO格式增强版，经Faster R-CNN验证感染细胞检测F1达0.88，为自动化诊断提供高质量训练数据。
摘要：
在吉姆萨染色血涂片中准确检测Plasmodium falciparum（恶性疟原虫）是可靠疟疾诊断的重要组成部分，尤其在发展中国家。基于深度学习的目标检测方法已展现出自动化疟疾诊断的巨大潜力，但其应用受限于缺乏带详细实例级注释的数据集。本研究提出公开可用的NIH疟疾数据集的增强版，包含COCO格式的详细边界框注释以支持目标检测训练。我们通过训练Faster R-CNN模型检测感染和未感染红细胞及白细胞，验证了修订后的注释。在原始数据集上的交叉验证显示，感染细胞检测的F1分数高达0.88。这些结果强调了注释数量和一致性的重要性，并证明自动化注释优化结合针对性人工修正可生成质量足够的训练数据，实现稳健的检测性能。更新后的注释集可通过Zenodo公开获取：https://doi.org/10.5281/zenodo.17514694
英文摘要：
Accurate detection of Plasmodium falciparum in Giemsa-stained blood smears is an essential component of reliable malaria diagnosis, especially in developing countries. Deep learning-based object detection methods have demonstrated strong potential for automated Malaria diagnosis, but their adoption is limited by the scarcity of datasets with detailed instance-level annotations. In this work, we present an enhanced version of the publicly available NIH malaria dataset, with detailed bounding box annotations in COCO format to support object detection training. We validated the revised annotations by training a Faster R-CNN model to detect infected and non-infected red blood cells, as well as white blood cells. Cross-validation on the original dataset yielded F1 scores of up to 0.88 for infected cell detection. These results underscore the importance of annotation volume and consistency, and demonstrate that automated annotation refinement combined with targeted manual correction can produce training data of sufficient quality for robust detection performance. The updated annotations set is publicly available via Zenodo: https://doi.org/10.5281/zenodo.17514694
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