3 papers
cs.CV2026
On-Device Multi-Species Malaria Detection with Uncertainty-Calibrated Slide-Level Aggregation
Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt +1
Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce. Automated diagnosis based on microscopy images thus has strong pot…
cs.CV2026
SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears
Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without…
cs.LG2026
Beyond validation loss: Clinically-tailored optimization metrics improve a model's clinical performance
Charles B. Delahunt, Courosh Mehanian, Daniel E. Shea +1
A key task in ML is to optimize models at various stages, e.g. by choosing hyperparameters or picking a stopping point. A traditional ML approach is to use validation loss, i.e. to…