Papers › Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies...
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang
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).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Property Prediction | ogbg-molhiv | DeepAUC | Ext. data | No | #4 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | DeepAUC | Number of params | 3444509 | #4 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | DeepAUC | Test ROC-AUC | 0.8352 ± 0.0054 | #4 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | DeepAUC | Validation ROC-AUC | 0.8238 ± 0.0061 | #4 of 43 | Archive leaderboard | report |
| Multi-Label Classification | CheXpert | DeepAUC-v1 | AVERAGE AUC ON 14 LABEL | 0.930 | #2 of 226 | Archive leaderboard | report |
| Multi-Label Classification | CheXpert | DeepAUC-v1 | NUM RADS BELOW CURVE | 2.800 | #2 of 226 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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