ESA-Net:An Efficient and Lightweight Model for Medical Image Segmentation
简介：Transformers have revolutionized medical image segmentation.However,their complexity leads to high parameter counts,increased FLOating-Point operations(FLOPs),and greater memory consumption,making them unsuitable for deployment on medical devices with limited computational resources.To overcome these limitations,we propose Efficient Shared Attention Network(ESA-Net),a lightweight and efficient model that achieves a favorable balance between accuracy and efficiency.ESA-Net adopts an encoder-decoder architecture,where the encoder incorporates an ESA module.This module leverages Content-Aware Position Encoding(CAPE)to enhance contextual sensitivity during feature extraction.The lightweight multi-scale decoder,based entirely on All Multi-Layer Perceptrons(All-MLP),ensures efficient reconstruction of segmentation maps.Experiments on the Synapse,ISIC17,ISIC18,and ACDC datasets validate the effectiveness of ESA-Net in multimodal medical image segmentation.For instance,on the Synapse dataset,ESA-Net achieves a dice score of 80.10％and reduces the Hausdorff distance to 15.34 mm.Moreover,ESA-Net demonstrates superior parameter efficiency,utilizing only 46％of the parameters of the Swin_UMamba model while maintaining comparable accuracy.These results highlight ESA-Net as a practical and deployable solution for medical image segmentation in resource-constrained environments.展开
学者：HaiquanLiuMingcanCenChongZhangAngelaAnShuxiangSong
关键词：TransformerMedical image segmentationshare attentionlightweight
在线出版日期：2026-05-14 （网站首发日期）
