论文标题
与Foveated Pyramid的注意一起定位头部计量X射线标志
Locating Cephalometric X-Ray Landmarks with Foveated Pyramid Attention
论文作者
论文摘要
CNN最初是受人类视力的启发，在关键方面有所不同：它们均匀地采样，而不是在焦点中具有最高密度。对于非常大的图像，这使得训练难以置信，因为激活图所需的内存和计算在图像的侧面长度上二次缩放。我们提出了一种基于图像金字塔的方法，该方法可以提取输入图像的狭窄瞥见，并迭代地完善它们以完成回归任务。为了协助高准确回归，我们引入了一种新型的中间表示，我们称为“空间化特征”。我们的方法以侧面长度对数进行对数，因此它可以与非常大的图像一起使用。我们将我们的方法应用于头孢胺X射线里程碑检测并获得最先进的结果。
CNNs, initially inspired by human vision, differ in a key way: they sample uniformly, rather than with highest density in a focal point. For very large images, this makes training untenable, as the memory and computation required for activation maps scales quadratically with the side length of an image. We propose an image pyramid based approach that extracts narrow glimpses of the of the input image and iteratively refines them to accomplish regression tasks. To assist with high-accuracy regression, we introduce a novel intermediate representation we call 'spatialized features'. Our approach scales logarithmically with the side length, so it works with very large images. We apply our method to Cephalometric X-ray Landmark Detection and get state-of-the-art results.
