Digitized Thin Blood Films for Sickle Cell Disease Detection

<div>If you plan on using this dataset, please cite : P. Manescu, C. Bendkowski, R. Claveau, M. Elmi, B.J. Brown, V. Pawar, M. Shaw and D. Fernandez-Reyes, <b>A weakly supervised deep learning approach for detecting malaria and sickle cells in blood films </b>, MICCAI (2020). </div><div><br></div><div><b> Image acquistion</b></div><div>Images were captured with custom built brightfield microscope fitted with a 100X/1.4NA objective lens, a motorized x-y sample positioning stage and a color camera.</div><div>z-stacks were projected onto a single (xy) plane using a wavelet-based Extended Depth of Field (EDoF) algorithm.</div><div><br></div><div><b>Clinical diagnosis</b></div><div>Hemoglobin electrophoresis was used to obtain the haemoglobin phenotype and test patients for Sickle Cell Disease (SCD). sickle_slides_new_march.txt contains the corresponding labels. <br></div><div><br></div><div><b>Ethical Statement. <br></b></div><div>The internationally recognized ethics committee at the Institute for Advanced Medical Research and Training (IAMRAT) of the College of Medicine, University of Ibadan (COMUI) approved this research with permit numbers: UI/EC/10/0130, UI/EC/19/0110. Parents and/or guardians of study participants gave informed written consent in accordance with the World Medical Association ethical principles for research involving human subjects.<br></div><div><br></div><div><br></div>