Malaria Thick Blood Smears

We photographed Giemsa-stained thick blood smear slides from 150 P. falciparum infected patients at Chittagong Medical College Hospital, Bangladesh, using a smartphone camera for the different microscopic field of views. Images are captured with 100x magnification in RGB color space with a 3024×4032 pixel resolution. An expert slide reader manually annotated each image at the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand. We de-identified all images andtheir annotations, and archived them at the National Library of Medicine (IRB#12972). Reference:We request that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA) and cite the following publication, which has used the data for parasite detection and classification:Feng Yang, Mahdieh Poostchi, Hang Yu, Zhou Zhou, Kamolrat Silamut, Jian Yu, Richard J Maude, Stefan Jaeger, Sameer Antani. Deep Learning for Smartphone-based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2019 Sep 23. doi: 10.1109/JBHI.2019.2939121.

我们于孟加拉国吉大港医学院附属医院（Chittagong Medical College Hospital），针对150例感染恶性疟原虫（*Plasmodium falciparum*，缩写P. falciparum）的患者，使用智能手机摄像头拍摄其吉姆萨染色（Giemsa-stained）厚血膜涂片玻片，采集不同显微镜视野下的图像。图像采用100倍放大倍率（100x magnification）拍摄，色彩空间为RGB（RGB color space），分辨率为3024×4032像素。泰国曼谷玛希隆-牛津热带医学研究单元（Mahidol-Oxford Tropical Medicine Research Unit, MORU）的资深玻片阅片专家对每张图像进行了人工标注。我们对所有图像及其标注信息进行了去标识化处理，并将其存档于美国国家医学图书馆（National Library of Medicine，IRB#12972）。
参考文献说明：使用本数据集生成的研究成果，请注明数据来源为美国国家医学图书馆、美国国立卫生研究院（马里兰州贝塞斯达，美国），并引用以下已利用该数据开展寄生虫检测与分类研究的论文：Feng Yang, Mahdieh Poostchi, Hang Yu, Zhou Zhou, Kamolrat Silamut, Jian Yu, Richard J Maude, Stefan Jaeger, Sameer Antani. 《基于智能手机的厚血膜涂片疟原虫检测的深度学习方法》，发表于《IEEE生物医学与健康信息学期刊》（IEEE Journal of Biomedical and Health Informatics），2019年9月23日，DOI: 10.1109/JBHI.2019.2939121.