文献类型：期刊文献

英文题名：An Efficient and Rapid Medical Image Segmentation Network

作者：Su, Diwei[1];Luo, Jianxu[1];Fei, Cheng[1]

机构：[1]East China Univ Sci & Technol, Coll Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：28

期号：5

起止页码：2979

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：;EI(收录号：20241215768332);WOS:【SCI-EXPANDED(收录号:WOS:001221547700026)】；

语种：英文

外文关键词：Transformers; Image segmentation; Computational modeling; Task analysis; Feature extraction; Computational complexity; Decoding; Lightweight; transformer; attention mechanism; medical image segmentation

摘要：Accurate medical image segmentation is an essential part of the medical image analysis process that provides detailed quantitative metrics. In recent years, extensions of classical networks such as UNet have achieved state-of-the-art performance on medical image segmentation tasks. However, the high model complexity of these networks limits their applicability to devices with constrained computational resources. To alleviate this problem, we propose a shallow hierarchical Transformer for medical image segmentation, called SHFormer. By decreasing the number of transformer blocks utilized, the model complexity of SHFormer can be reduced to an acceptable level. To improve the learned attention while keeping the structure lightweight, we propose a spatial-channel connection module. This module separately learns attention in the spatial and channel dimensions of the feature while interconnecting them to produce more focused attention. To keep the decoder lightweight, the MLP-D module is proposed to progressively fuse multi-scale features in which channels are aligned using Multi-Layer Perceptron (MLP) and spatial information is fused by convolutional blocks. We first validated the performance of SHFormer on the ISIC-2018 dataset. Compared to the latest network, SHFormer exhibits comparable performance with 15 times fewer parameters, 30 times lower computational complexity and 5 times higher inference efficiency. To test the generalizability of SHFormer, we introduced the polyp dataset for additional testing. SHFormer achieves comparable segmentation accuracy to the latest network while having lower computational overhead.