Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information. (arXiv:1908.01901v2 [cs.LG] UPDATED)

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), …

arxiv film images information malaria patient