SATr: Slice Attention with Transformer for Universal Lesion Detection

• URL: http://arxiv.org/abs/2203.07373v1
• Date: Sun, 13 Mar 2022 03:37:27 GMT
• Title: SATr: Slice Attention with Transformer for Universal Lesion Detection
• Authors: Han Li, Long Chen, Hu Han, S. Kevin Zhou
• Abstract summary: Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. We propose a novel Slice Attention Transformer (SATr) block which can be easily plugged into convolution-based ULD backbones. Experiments with five state-of-the-art methods show that the proposed SATr block can provide an almost free boost to lesion detection accuracy.
• Score: 39.90420943500884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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