Driving down Poisson error can offset classification error in clinical tasks

no code implementations • 9 May 2024 • Charles B. Delahunt, Courosh Mehanian, Matthew P. Horning

To this end, it may have the option to offset its lower accuracy by increasing sample size to reduce Poisson error, and thus attain the same net clinical performance as a perfectly accurate human limited by smaller sample size.

Metrics to guide development of machine learning algorithms for malaria diagnosis

no code implementations • 14 Sep 2022 • Charles B. Delahunt, Noni Gachuhi, Matthew P. Horning

Two factors in particular are crucial to developing algorithms translatable to clinical field settings: (i) Clear understanding of the clinical needs that ML solutions must accommodate; and (ii) task-relevant metrics for guiding and evaluating ML models.

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

no code implementations • 5 Aug 2019 • Charles B. Delahunt, Mayoore S. Jaiswal, Matthew P. Horning, Samantha Janko, Clay M. Thompson, Sourabh Kulhare, Liming Hu, Travis Ostbye, Grace Yun, Roman Gebrehiwot, Benjamin K. Wilson, Earl Long, Stephane Proux, Dionicia Gamboa, Peter Chiodini, Jane Carter, Mehul Dhorda, David Isaboke, Bernhards Ogutu, Wellington Oyibo, Elizabeth Villasis, Kyaw Myo Tun, Christine Bachman, David Bell, Courosh Mehanian

Malaria is a life-threatening disease affecting millions.