DL4Malaria: Deep Learning Approaches for the Automated Detection and Characterisation of Malaria Parasites on Thin Blood Smear Images
Ana Filipa Sampaio · Open Repository of the University of Porto (University of Porto) · 2019
First of all, I would like to thank everyone at Fraunhofer AICOS, for giving me the opportunity to develop my dissertation in such a stimulating environment and to contribute to a project as fascinating as the MalariaScope.It was a sincere pleasure to be able to work everyday with a smile and maintain the motivation throughout the whole dissertation's period.Among all the outstanding professionals of this institute, I address a special thank you note to my supervisors: to Doctor Luís Rosado, for the insightful information regarding all aspects of the Malaria disease and the MalariaScope framework, as well as the patient orientation and encouragement in the last "sprint"; and to José Faria, for the very useful advices and tips, all the essential technical help and the endless availability demonstrated (even after following a diverging professional path).To Professor Luís Teixeira, I am truly grateful not only for the provided guidance (that was fundamental for the accomplishment of this dissertation), but also for introducing me to the machine learning field in such an interesting manner and for giving me the possibility of growing as a professional in this area.This work would also not have been possible without the continuous support and active contribution of Fraunhofer's clinical partners.So a very special word of gratitude has to be done to