Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information

--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) quanti-tate 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. Quanti-tation 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. Index T erms --malaria, automated microscopy, deep neural networks, gradient boosted trees I. I NTRODUCTION Malaria is a mosquito-borne disease caused by Plasmodium species ( P . Manual microscopy examination of Giemsa-stained blood films is a widespread malaria diagnosis method. Key use-cases include diagnosis; species identification (ID) to guide treatment [2]; and quantitation of parasites for drug resistance studies, to track how fast a drug clears parasites from the blood. However, a lack of training, high inter-sample variability in preparation and presentation, and difficult field conditions can result in poor accuracy [3], [4]. Also, lack of trained personnel limits the number of drug resistance sentinel sites. Malaria microscopy is a difficult task for automated image-processing and machine learning (ML) systems for two reasons: Field-prepared blood films vary widely in quality and presentation; and parasites are small (with feature size close to optical limits of resolution), rare, highly variable, and easily confused with non-parasite objects (artifacts). But it is also a high-value target, due to the potential benefit for so many people, and also because automated systems have some concrete advantages: They can be widely deployed, solving the expert-training bottleneck; they can examine more blood volume per patient, reducing variability in quantitation caused by Poisson statistics; and their results are reproducible.