4 citations · 10 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz
Softmax is a standard final layer used in Neural Nets (NNs) to summarize information encoded in the trained NN and return a prediction. However, Softmax leverages only a subset of…