Author

Morgan May

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Open access 2026

When Data Augmentation Improves the Calibration and Accuracy of Deep Neural Networks

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

#artificial intelligence Preprint Oct 2026

Impact of Data Augmentation on Confidence Calibration in Melanoma Classification

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

#artificial intelligence Preprint Oct 2026

On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis

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