PI: Rene Vidal (Biomedical Engineering, WSE)

Co-I: Benjamin Haeffele (MINDS), Matthew Ippolito (Medicine, SOM)

The current proposal will build on computer vision techniques recently developed by Dr. Haeffele in the Vidal Laboratory of the Johns Hopkins Whiting School of Engineering, to detect and classify blood cells in low-resolution lens-free images with a reduced volume of annotated data. This project will extend such computer vision methodology for data mining of malaria microscopy data in patient samples from antimalarial drug trials conducted by the Johns Hopkins Malaria Research Institute at the Johns Hopkins Bloomberg School of Public Health. Linking computer vision-based machine learning algorithms to malaria pharmacology promises to unlock novel insights into the effect of drugs on malaria parasites while establishing a new evaluative tool for the assessment and understanding of malaria and its treatment.