4 citations · 7 across the 5 of their papers we have counts for
5 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…
Metrics to guide development of machine learning algorithms for malaria diagnosis
Charles B. Delahunt, Noni Gachuhi, Matthew P. Horning
Automated malaria diagnosis is a difficult but high-value target for machine learning (ML), and effective algorithms could save many thousands of children's lives. However, current…
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
Charles B. Delahunt, Mayoore S. Jaiswal, Matthew P. Horning +22
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…