Impact of Data Augmentation on Confidence Calibration in Melanoma Classification

Excerpt

arXiv:2610.06146v1 Announce Type: new Abstract: 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 data augmentation. In addition, data augmentation as a method for synthetically increasing the size of the dataset has been proven to