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, A weakly supervised deep learning approach for detecting malaria and sickle cells in blood films , MICCAI (2020).
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.
z-stacks were projected onto a single (xy) plane using a wavelet-based Extended Depth of Field (EDoF) algorithm.
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.
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.
This work was supported by the College of Medicine of the University of Ibadan, Ibadan, Nigeria; the UK Medical Research Council (MC_U117585869); Department of Computer Science, Faculty of Engineering Sciences of University College London, United Kingdom and UK Engineering and Physical Sciences Research Council (EP/P028608/1).