{"ID":2856272,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11259","arxiv_id":"2510.11259","title":"DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation","abstract":"In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limited structural representation and insufficient contextual modeling, affecting generalization in complex clinical scenarios. We propose the DTEA model, featuring a new skip connection framework with the Semantic Topology Reconfiguration (STR) and Entropic Perturbation Gating (EPG) modules. STR reorganizes multi-scale semantic features into a dynamic hypergraph to better model cross-resolution anatomical dependencies, enhancing structural and semantic representation. EPG assesses channel stability after perturbation and filters high-entropy channels to emphasize clinically important regions and improve spatial attention. Extensive experiments on three benchmark datasets show our framework achieves superior segmentation accuracy and better generalization across various clinical settings. The code is available at \\href{https://github.com/LWX-Research/DTEA}{https://github.com/LWX-Research/DTEA}.","short_abstract":"In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limited structural representation and insufficient contextual modeling, affecting generalization in complex clinical scenarios. We propose the DTE...","url_abs":"https://arxiv.org/abs/2510.11259","url_pdf":"https://arxiv.org/pdf/2510.11259v1","authors":"[\"Weixuan Li\",\"Quanjun Li\",\"Guang Yu\",\"Song Yang\",\"Zimeng Li\",\"Chi-Man Pun\",\"Yupeng Liu\",\"Xuhang Chen\"]","published":"2025-10-13T10:50:41Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":608330,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2856272,"paper_url":"https://arxiv.org/abs/2510.11259","paper_title":"DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation","repo_url":"https://github.com/LWX-Research/DTEA","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}