{"ID":3053379,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T03:31:06.711308811Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04427","arxiv_id":"2606.04427","title":"Implicit Fuzzification via Bounded Noise Injection for Robust Medical Image Segmentation","abstract":"Image segmentation remains fundamentally limited by boundary ambiguity arising from sampling-induced information loss and inherent uncertainty in pixel-wise labeling. Although encoder-decoder architectures such as U-Net achieve strong performance, they often produce overconfident predictions that fail to capture transition-region ambiguity. To address this issue, we propose \\textbf{NoiseUNet}, a simple yet effective framework that injects bounded perturbations into skip connections to regularize cross-scale feature fusion. This mechanism enforces robustness to local feature variations and promotes boundary-aware representations. Theoretically, the perturbation induces an implicit fuzzification effect, yielding soft, data-driven memberships without requiring explicit fuzzy modeling. We further introduce \\textbf{ThyR}, a real-world thyroid ultrasound dataset with inherently ambiguous boundaries. Experiments demonstrate that NoiseUNet consistently improves both segmentation accuracy and boundary fidelity.","short_abstract":"Image segmentation remains fundamentally limited by boundary ambiguity arising from sampling-induced information loss and inherent uncertainty in pixel-wise labeling. Although encoder-decoder architectures such as U-Net achieve strong performance, they often produce overconfident predictions that fail to capture transi...","url_abs":"https://arxiv.org/abs/2606.04427","url_pdf":"https://arxiv.org/pdf/2606.04427v1","authors":"[\"Bisheng Tang\",\"Zhangfeng Ma\",\"Chuchu Zhai\",\"Feng Dong\",\"Yaoqun Wu\",\"Ammar Oad\",\"Yifei Peng\"]","published":"2026-06-03T04:17:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}