论文标题
通用病变检测的边界图
Bounding Maps for Universal Lesion Detection
论文作者
论文摘要
计算机断层扫描中的通用病变检测（ULD）在计算机辅助诊断系统中起着至关重要的作用。许多检测方法使用可能的边界框（或锚）作为建议，为ULD取得了出色的结果。但是，经验证据表明，使用基于锚的建议会导致高阳性（FP）率。在本文中，我们提出了一种框对图方法，以表示一个边界框，该框具有三个软连续地图，并在x-，y-和xy-方向上进行边界。边界图（BMS）用于两阶段锚点的ULD框架以降低FP速率。在区域提案网络的第1阶段中，我们用相应的XY方向BM替换了尖锐的二元锚定标签，因此现在分级为正锚。在第2阶段，我们添加了一个分支，该分支将我们的连续BMS沿X和Y-指示进行，以额外监督详细的位置。当我们的方法嵌入三种基于两阶段的基于两阶段的锚检测方法时，可以提高自由检测精度（例如，在4 fps时在4 fps时提高1.68％至3.85％的敏感性），而没有额外的推理时间。
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis systems. Many detection approaches achieve excellent results for ULD using possible bounding boxes (or anchors) as proposals. However, empirical evidence shows that using anchor-based proposals leads to a high false-positive (FP) rate. In this paper, we propose a box-to-map method to represent a bounding box with three soft continuous maps with bounds in x-, y- and xy- directions. The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate. In the 1 st stage of the region proposal network, we replace the sharp binary ground-truth label of anchors with the corresponding xy-direction BM hence the positive anchors are now graded. In the 2 nd stage, we add a branch that takes our continuous BMs in x- and y- directions for extra supervision of detailed locations. Our method, when embedded into three state-of-the-art two-stage anchor-based detection methods, brings a free detection accuracy improvement (e.g., a 1.68% to 3.85% boost of sensitivity at 4 FPs) without extra inference time.
