3D Medical image segmentation using parallel transformers

Qingsen Yan, Shengqiang Liu, Songhua Xu, Caixia Dong, Zongfang Li, Javen Qinfeng Shi, Yanning Zhang, Duwei Dai

Abstract: Highlights•We propose a novel deep neural network based on Transformer, named TransHRNet, which connects the different resolution streams in parallel and repeatedly exchanges the information across resolutions.•An Effective Transformer (EffTrans) is introduced to promote the performance, which uses the group linear transformations with an expand-reduce strategy and the spatial-reduction attention layer to further reduce the resource cost.•Our proposed method achieves higher performance than SoTA efficient medical image segmentation method with comparable computation cost.