  The clinical explainability of convolutional neural networks (CNN) heavily
relies on the joint interpretation of a model's predicted diagnostic label and
associated confidence. A highly certain or uncertain model can significantly
impact clinical decision-making. Thus, ensuring that confidence estimates
reflect the true correctness likelihood for a prediction is essential. CNNs are
often poorly calibrated and prone to overconfidence leading to improper
measures of uncertainty. This creates the need for confidence calibration.
However, accuracy and performance-based evaluations of CNNs are commonly used
as the sole benchmark for medical tasks. Taking into consideration the risks
associated with miscalibration is of high importance. In recent years, modern
augmentation techniques, which cut, mix, and combine images, have been
introduced. Such augmentations have benefited CNNs through regularization,
robustness to adversarial samples, and calibration. Standard augmentations
based on image scaling, rotating, and zooming, are widely leveraged in the
medical domain to combat the scarcity of data. In this paper, we evaluate the
effects of three modern augmentation techniques, CutMix, MixUp, and CutOut on
the calibration and performance of CNNs for medical tasks. CutMix improved
calibration the most while CutOut often lowered the level of calibration.
