论文标题
大规模强大的深度AUC最大化：关于医学图像分类的新替代损失和经验研究
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
论文作者
论文摘要
深度AUC最大化（DAM）是通过在数据集中最大化模型的AUC分数来学习深神经网络的新范式。 AUC最大化的大多数以前的作品都通过设计有效的随机算法来关注优化的观点，以及关于大型大坝在困难任务上的概括性能的研究。在这项工作中，我们旨在使大坝对有趣的现实世界应用（例如，医学图像分类）更加实用。首先，我们提出了AUC分数（称为AUC Min-Max-Max-Max-Margin损失或简称简称为简称AUC的Min-Max替代损失函数）的新的基于保证金的最大替代损失函数。它比常用的AUC方形损失更健壮，同时在大规模随机优化方面具有相同的优势。其次，我们对大坝方法进行了有关四个困难的医学图像分类任务的广泛实证研究，即（i）（i）胸部X射线图像的分类，用于识别许多威胁性疾病，（ii）鉴定黑色素瘤的皮肤病变图像的分类，（iii）乳腺癌筛查的乳腺X线照片分类，以识别乳腺癌的分类，以及（iv）识别图像tumorsing tumoring tumoring tumors tumorning tumoring tumoring tumoring tumors tumorning tumorning tumor的图像。我们的研究表明，所提出的大坝方法比优化这些医学图像分类任务上现有的AUC方形损失相比，提高了优化跨透明镜损失的性能，并且可以提高性能。具体来说，我们的大坝方法已于2020年8月31日获得了斯坦福·奇（Stanford Chexpert）竞赛的第一名。据我们所知，这是使大坝在大规模医疗图像数据集上取得成功的第一部作品。我们还进行了广泛的消融研究，以证明与基准数据集对AUC平方损失相比，新的AUC利润率损失的优势。提出的方法是在我们开源的图书馆libauc（www.libauc.org）中实现的。
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).
