论文标题
与医学成像分类系统应用的可概括性特征的对抗性训练显着性的对抗培训
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
论文作者
论文摘要
这项工作解决了一个中央机器学习问题的绩效降解问题（OOD）测试集。这个问题在基于医学成像的诊断系统中尤为明显，该系统似乎是准确的，但在新医院/数据集中进行测试时失败。最近的研究表明，该系统可能会学习快捷方式和非相关功能，而不是可推广的功能，即所谓的良好功能。我们假设对抗性训练可以消除快捷方式功能，而显着性训练可以滤除非相关功能。两者都是OOD测试集的性能降解的滋扰功能。因此，我们为深层神经网络制定了一种新颖的模型培训方案，以学习分类和/或检测任务的良好功能，以确保在OOD测试集上保持一致的概括性能。实验结果定性和定量地证明了我们使用基准CXR图像数据集在分类任务上的卓越性能。
This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based diagnosis system that appears to be accurate but fails when tested in new hospitals/datasets. Recent studies indicate the system might learn shortcut and non-relevant features instead of generalizable features, so-called good features. We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets. With that, we formulate a novel model training scheme for the deep neural network to learn good features for classification and/or detection tasks ensuring a consistent generalization performance on OOD test sets. The experimental results qualitatively and quantitatively demonstrate the superior performance of our method using the benchmark CXR image data sets on classification tasks.
