  Malaria is a life-threatening disease affecting millions. Microscopy-based
assessment of thin blood films is a standard method to (i) determine malaria
species and (ii) quantitate high-parasitemia infections. Full automation of
malaria microscopy by machine learning (ML) is a challenging task because
field-prepared slides vary widely in quality and presentation, and artifacts
often heavily outnumber relatively rare parasites. In this work, we describe a
complete, fully-automated framework for thin film malaria analysis that applies
ML methods, including convolutional neural nets (CNNs), trained on a large and
diverse dataset of field-prepared thin blood films. Quantitation and species
identification results are close to sufficiently accurate for the concrete
needs of drug resistance monitoring and clinical use-cases on field-prepared
samples. We focus our methods and our performance metrics on the field use-case
requirements. We discuss key issues and important metrics for the application
of ML methods to malaria microscopy.
