Malaria Thick Blood Smears

All rights are reserved by National Library of Medicine.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). Citation of the data: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 publications, which have used the data for parasite detection and classification:[1]. 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.

本数据集所有权利归美国国家医学图书馆（National Library of Medicine）所有。研究团队使用智能手机相机，于孟加拉国吉大港医学院附属医院为150例恶性疟原虫（Plasmodium falciparum, P. falciparum）感染患者拍摄了吉姆萨染色（Giemsa-stained）厚血膜涂片的不同显微镜视野图像。所有图像均以100倍放大倍率拍摄，采用RGB色彩空间，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）的资深涂片阅片师对每张图像进行了手动标注。研究团队对所有图像及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（伦理审查委员会编号：12972，IRB#12972）。数据引用规范：若使用本数据集产出研究成果并发表，请注明数据来源（美国国家医学图书馆、美国国立卫生研究院（National Institutes of Health），马里兰州贝塞斯达，美国），并引用以下已使用该数据集开展疟原虫检测与分类研究的文献：[1] 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深度学习方法. IEEE生物医学与健康信息学汇刊（IEEE Journal of Biomedical and Health Informatics）, 2019年9月23日. DOI: 10.1109/JBHI.2019.2939121.