1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
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…
How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound
Menghan Yu, Sourabh Kulhare, Courosh Mehanian +5
Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthc…