  Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural
network by maximizing the AUC score of the model on a dataset. Most previous
works of AUC maximization focus on the perspective of optimization by designing
efficient stochastic algorithms, and studies on generalization performance of
large-scale DAM on difficult tasks are missing. In this work, we aim to make
DAM more practical for interesting real-world applications (e.g., medical image
classification). First, we propose a new margin-based min-max surrogate loss
function for the AUC score (named as AUC min-max-margin loss or simply AUC
margin loss for short). It is more robust than the commonly used AUC square
loss, while enjoying the same advantage in terms of large-scale stochastic
optimization. Second, we conduct extensive empirical studies of our DAM method
on four difficult medical image classification tasks, namely (i) classification
of chest x-ray images for identifying many threatening diseases, (ii)
classification of images of skin lesions for identifying melanoma, (iii)
classification of mammogram for breast cancer screening, and (iv)
classification of microscopic images for identifying tumor tissue. Our studies
demonstrate that the proposed DAM method improves the performance of optimizing
cross-entropy loss by a large margin, and also achieves better performance than
optimizing the existing AUC square loss on these medical image classification
tasks. Specifically, our DAM method has achieved the 1st place on Stanford
CheXpert competition on Aug. 31, 2020. To the best of our knowledge, this is
the first work that makes DAM succeed on large-scale medical image datasets. We
also conduct extensive ablation studies to demonstrate the advantages of the
new AUC margin loss over the AUC square loss on benchmark datasets. The
proposed method is implemented in our open-sourced library LibAUC
(www.libauc.org).
