# AUC-mixup: Deep AUC Maximization with Mixup

arXiv: https://arxiv.org/abs/2310.11693 · HTML: https://mlsanity.com/paper/2310.11693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification](https://mlsanity.com/paper/2012.03173/) (2020)
- [LibAUC: A Deep Learning Library for X-Risk Optimization](https://mlsanity.com/paper/2306.03065/) (2023)

## Abstract (arXiv)

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.