학술논문
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
Document Type
Working Paper
Author
Delahunt, Charles B.; Jaiswal, Mayoore S.; Horning, Matthew P.; Janko, Samantha; Thompson, Clay M.; Kulhare, Sourabh; Hu, Liming; Ostbye, Travis; Yun, Grace; Gebrehiwot, Roman; Wilson, Benjamin K.; Long, Earl; Proux, Stephane; Gamboa, Dionicia; Chiodini, Peter; Carter, Jane; Dhorda, Mehul; Isaboke, David; Ogutu, Bernhards; Oyibo, Wellington; Villasis, Elizabeth; Tun, Kyaw Myo; Bachman, Christine; Bell, David; Mehanian, Courosh
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Abstract
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.
Comment: 16 pages, 13 figures
Comment: 16 pages, 13 figures