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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…
cs.LG2024
Driving down Poisson error can offset classification error in clinical tasks
Charles B. Delahunt, Courosh Mehanian, Matthew P. Horning
Medical machine learning algorithms are typically evaluated based on accuracy vs. a clinician-defined ground truth, a reasonable initial choice since trained clinicians are usually…