{"ID":2843933,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07057","arxiv_id":"2511.07057","title":"TauFlow: Dynamic Causal Constraint for Complexity-Adaptive Lightweight Segmentation","abstract":"Deploying lightweight medical image segmentation models on edge devices presents two major challenges: 1) efficiently handling the stark contrast between lesion boundaries and background regions, and 2) the sharp drop in accuracy that occurs when pursuing extremely lightweight designs (e.g., \u003c0.5M parameters). To address these problems, this paper proposes TauFlow, a novel lightweight segmentation model. The core of TauFlow is a dynamic feature response strategy inspired by brain-like mechanisms. This is achieved through two key innovations: the Convolutional Long-Time Constant Cell (ConvLTC), which dynamically regulates the feature update rate to \"slowly\" process low-frequency backgrounds and \"quickly\" respond to high-frequency boundaries; and the STDP Self-Organizing Module, which significantly mitigates feature conflicts between the encoder and decoder, reducing the conflict rate from approximately 35%-40% to 8%-10%.","short_abstract":"Deploying lightweight medical image segmentation models on edge devices presents two major challenges: 1) efficiently handling the stark contrast between lesion boundaries and background regions, and 2) the sharp drop in accuracy that occurs when pursuing extremely lightweight designs (e.g., \u003c0.5M parameters). To addre...","url_abs":"https://arxiv.org/abs/2511.07057","url_pdf":"https://arxiv.org/pdf/2511.07057v1","authors":"[\"Zidong Chen\",\"Fadratul Hafinaz Hassan\"]","published":"2025-11-10T12:47:21Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}