论文标题
Mirst-DM：多稳定RST带有滴度最大层用于良好的乳腺癌分类
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
论文作者
论文摘要
强大的自我训练（RST）可以增强图像分类模型的对抗鲁棒性，而无需显着牺牲模型的通用性。但是，RST和其他最先进的防御方法未能保留普遍性，并在小型医学图像集上重现其良好的对抗性鲁棒性。在这项工作中，我们提出了具有滴度最大层的多命名RST，即Mirst-DM，它涉及训练期间的一系列迭代生成的对抗实例，以学习小型数据集上的更轻松的决策边界。提出的滴度最大层消除了不稳定的功能，并有助于学习图像扰动的强大表示形式。使用带有1,190张图像的小型乳房超声数据集对所提出的方法进行了验证。结果表明，所提出的方法实现了对三种普遍攻击的最先进的对抗性鲁棒性。
Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability. However, RST and other state-of-the-art defense approaches failed to preserve the generalizability and reproduce their good adversarial robustness on small medical image sets. In this work, we propose the Multi-instance RST with a drop-max layer, namely MIRST-DM, which involves a sequence of iteratively generated adversarial instances during training to learn smoother decision boundaries on small datasets. The proposed drop-max layer eliminates unstable features and helps learn representations that are robust to image perturbations. The proposed approach was validated using a small breast ultrasound dataset with 1,190 images. The results demonstrate that the proposed approach achieves state-of-the-art adversarial robustness against three prevalent attacks.
