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Vision transformer (ViT)-based models have demonstrated strong performance in medical image segmentation by modeling long-range dependencies through self-attention mechanisms, but these models still face several challenges, including sensitivity to noise, limited ability to capture fine-grained structures, and high computational overhead caused by dense token representations. To address these limitations, we propose a frequency-aware sparse token vision transformer (FS-ViT) for medical image segmentation. The proposed framework incorporates two key modules: a frequency-aware sparse self-attention (FS-SA) module and a frequency-aware MLP (FA-MLP) module. The FS-SA module first transforms spatial tokens into the frequency domain using Fourier transformation and then calculates self-attention scores to capture robust global representations while reducing sensitivity to spatial noise. In addition, a sparse token mechanism is introduced to eliminate redundant tokens, thus improving computational efficiency by lowering overhead. Additionally, the FA-MLP module is proposed to enhance feature representation by modeling fine-grained structures in the frequency domain by employing Fourier-based feature learning and a learnable query mechanism. The FS-SA and FA-MLP modules are integrated into a U-shaped encoder\u2013decoder ViT architecture. The proposed method is evaluated on five medical image segmentation tasks, and the experimental results demonstrate that FS-ViT achieved superior performance compared with other state-of-the-art segmentation methods, highlighting its effectiveness and generalizability for robust and efficient medical image segmentation.<\/jats:p>","DOI":"10.1007\/s44163-026-02129-8","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:58:48Z","timestamp":1788764328000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Frequency-aware sparse token vision transformer for medical image segmentation"],"prefix":"10.1007","volume":"6","author":[{"given":"Junlin","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2129_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. 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