论文标题
使用深神经网络分类器对医学图像的细分进行偷偷攻击
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers
论文作者
论文摘要
我们不使用当前的深度学习分割模型（例如UNET和变体），而是使用训练有素的卷积神经网络（CNN）分类器来解决分割问题，该分类器会自动从图像中提取重要特征进行分类。这些提取的特征可以可视化并使用梯度加权类激活映射（GRAD-CAM）形成热图。这项研究测试了是否可以使用热图来分割分类目标。我们还提出了用于热图的评估方法。也就是说，使用由热图过滤的图像重新培训CNN分类器并检查其性能。我们使用均值系数来评估分割结果。我们的实验结果表明，热图可以定位和分段部分肿瘤区域。但是，仅使用来自CNN分类器的热图可能不是分割的最佳方法。我们已经证实，CNN分类器的预测主要取决于肿瘤区域，而Grad-CAM热图中的黑暗区域也有助于分类。
Instead of using current deep-learning segmentation models (like the UNet and variants), we approach the segmentation problem using trained Convolutional Neural Network (CNN) classifiers, which automatically extract important features from images for classification. Those extracted features can be visualized and formed into heatmaps using Gradient-weighted Class Activation Mapping (Grad-CAM). This study tested whether the heatmaps could be used to segment the classified targets. We also proposed an evaluation method for the heatmaps; that is, to re-train the CNN classifier using images filtered by heatmaps and examine its performance. We used the mean-Dice coefficient to evaluate segmentation results. Results from our experiments show that heatmaps can locate and segment partial tumor areas. But use of only the heatmaps from CNN classifiers may not be an optimal approach for segmentation. We have verified that the predictions of CNN classifiers mainly depend on tumor areas, and dark regions in Grad-CAM's heatmaps also contribute to classification.
