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However, the prevalence of small targets still poses great challenges for accurate segmentation. In this paper, we propose a novel locally enhanced transformer network (LET-Net) that combines the strengths of transformer and convolution to address this issue. LET-Net utilizes a pyramid vision transformer as its encoder and is further equipped with two novel modules to learn more powerful feature representation. Specifically, we design a feature-aligned local enhancement module, which encourages discriminative local feature learning on the condition of adjacent-level feature alignment. Moreover, to effectively recover high-resolution spatial information, we apply a newly designed progressive local-induced decoder. This decoder contains three cascaded local reconstruction and refinement modules that dynamically guide the upsampling of high-level features by their adaptive reconstruction kernels and further enhance feature representation through a split-attention mechanism. Additionally, to address the severe pixel imbalance for small targets, we design a mutual information loss that maximizes task-relevant information while eliminating task-irrelevant noises. Experimental results demonstrate that our LET-Net provides more effective support for small target segmentation and achieves state-of-the-art performance in polyp and breast lesion segmentation tasks.<\/jats:p>","DOI":"10.1007\/s00530-023-01165-z","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T12:02:27Z","timestamp":1693915347000},"page":"3847-3861","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["LET-Net: locally enhanced transformer network for medical image segmentation"],"prefix":"10.1007","volume":"29","author":[{"given":"Na","family":"Ta","sequence":"first","affiliation":[]},{"given":"Haipeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xianzhu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Nuo","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"1165_CR1","doi-asserted-by":"publisher","unstructured":"Fang, Y., Chen, C., Yuan, Y., Tong, R.K.: Selective feature aggregation network with area-boundary constraints for polyp segmentation. 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