  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.
