  Universal Lesion Detection (ULD) in computed tomography plays an essential
role in computer-aided diagnosis. Promising ULD results have been reported by
multi-slice-input detection approaches which model 3D context from multiple
adjacent CT slices, but such methods still experience difficulty in obtaining a
global representation among different slices and within each individual slice
since they only use convolution-based fusion operations. In this paper, we
propose a novel Slice Attention Transformer (SATr) block which can be easily
plugged into convolution-based ULD backbones to form hybrid network structures.
Such newly formed hybrid backbones can better model long-distance feature
dependency via the cascaded self-attention modules in the Transformer block
while still holding a strong power of modeling local features with the
convolutional operations in the original backbone. Experiments with five
state-of-the-art methods show that the proposed SATr block can provide an
almost free boost to lesion detection accuracy without extra hyperparameters or
special network designs.
