  While deep AUC maximization (DAM) has shown remarkable success on imbalanced
medical tasks, e.g., chest X-rays classification and skin lesions
classification, it could suffer from severe overfitting when applied to small
datasets due to its aggressive nature of pushing prediction scores of positive
data away from that of negative data. This paper studies how to improve
generalization of DAM by mixup data augmentation -- an approach that is widely
used for improving generalization of the cross-entropy loss based deep learning
methods. %For overfitting issues arising from limited data, the common approach
is to employ mixup data augmentation to boost the models' generalization
performance by enriching the training data. However, AUC is defined over
positive and negative pairs, which makes it challenging to incorporate mixup
data augmentation into DAM algorithms. To tackle this challenge, we employ the
AUC margin loss and incorporate soft labels into the formulation to effectively
learn from data generated by mixup augmentation, which is referred to as the
AUC-mixup loss. Our experimental results demonstrate the effectiveness of the
proposed AUC-mixup methods on imbalanced benchmark and medical image datasets
compared to standard DAM training methods.
