aiqbench analysis
This study investigates how data augmentation techniques influence the confidence calibration of neural networks used for melanoma classification on imbalanced datasets. By comparing models trained with and without augmentation, the research demonstrates that specific synthetic data strategies can significantly reduce expected calibration error while maintaining diagnostic accuracy. These findings are critical for deploying reliable AI systems in high-stakes medical imaging where quantifying predictive uncertainty is essential for patient safety.
Impact of Data Augmentation on Confidence Calibration in Melanoma Classification
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 improve the performance of models trained on imbalanced datasets. However, the impact of data augmentation, as a transformation of a part of the original data, on calibration of models trained on imbalanced datasets, in particular in melanoma classification is under-explored. We train neural networks on SIIM-ISIC 2020 melanoma classification dataset under two conditions: with and without data augmentation, and compare the differences in AUC and expected calibration error (ECE) in both scenarios. Our results shows improvements in uncertainty calibration using different augmentation methods.
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 improve the performance of models trained on imbalanced datasets. However, the impact of data augmentation, as a transformation of a part of the original data, on calibration of models trained on imbalanced datasets, in particular in melanoma classification is under-explored. We train neural networks on SIIM-ISIC 2020 melanoma classification dataset under two conditions: with and without data augmentation, and compare the differences in AUC and expected calibration error (ECE) in both scenarios. Our results shows improvements in uncertainty calibration using different augmentation methods.
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
arXiv:2610.06146 [cs.AI]
(or arXiv:2610.06146v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.06146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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