Accurately quantifying the predictive confidence or improving model calibration has recently attracted increasing attention, in contrast to focusing only on maximizing model accuracy. This is essential especially for safety-sensitive applications. Among various techniques for calibration improvement, some data augmenta...
Morgan May, Simon Caton, Pierpaolo Dondio· IEEE Access· 0 citations
Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particular in melanoma diagnosis, where accurate uncertainty quantification can have significant implications for patient care. One of the methods for calibration improvement is d...
Morgan May, Simon Caton, Pierpaolo Dondio· 0 citations
F focal loss delivers a good combination of performance in terms of AUC and uncertainty calibration in terms of expected calibration error (ECE) simultaneously, and the results suggest that focal loss delivers a good combination of performance in terms of AUC and uncertainty calibration in terms of expected calibration...
Morgan May, Pierpaolo Dondio, Simon Caton· 0 citations
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