发表机构
College of Computer Science, Chongqing University（重庆大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对皮肤病变分割中全局上下文与边界细节难以兼顾的问题，提出即插即用的MoSSGate模块，融合边界感知门控、外部记忆调制和并行状态空间建模，在ISIC 2017/2018上以更低计算量实现最先进的mIoU和Dice性能。
AI 中文摘要
准确的皮肤病变分割对于可靠的计算机辅助皮肤病学诊断至关重要，然而现有的基于卷积和基于Transformer的模型在有限的计算预算下，往往难以同时捕捉长距离空间依赖和精细的边界细节。全局上下文建模与边界感知定位之间的这种权衡，常常导致过度分割、碎片化预测或遗漏细小的外周结构。为了解决这一挑战，我们提出了MoSSGate，一种即插即用的U-Net模块，它整合了(i)边界感知的空间门控，以将长距离传播限制在信息丰富的区域；(ii)外部记忆调制器，提供样本自适应的动态控制；以及(iii)并行二维状态空间建模，以线性复杂度实现高效的全局上下文聚合。所提出的设计能够实现自适应、上下文感知的信息传播，同时保持清晰准确的病变边界。在ISIC 2017和ISIC 2018基准上的大量实验表明，该方法达到了最先进的准确性并具有强大的效率，分别实现了86.3%和85.9%的mIoU以及92.6%和90.6%的Dice系数，同时所需的FLOPs远少于大多数竞争的基于CNN的方法。这些结果突显了在高分辨率医学图像分割中良好的精度-效率权衡。
英文摘要
Accurate skin lesion segmentation is crucial for reliable computer-aided dermatological diagnosis, yet existing convolutional and transformer-based models often struggle to jointly capture long-range spatial dependencies and fine boundary details under limited computational budgets. This trade-off between global context modeling and boundary-aware localization frequently leads to over-segmentation, fragmented predictions, or missing thin peripheral structures. To address this challenge, we propose MoSSGate, a plug-and-play module for U-Net that integrates (i) boundary-aware spatial gating to restrict long-range propagation to informative regions, (ii) an external memory modulator that provides sample-adaptive dynamic control, and (iii) parallel 2D state-space modeling for efficient global context aggregation with linear complexity. The proposed design enables adaptive, context-aware information propagation while preserving sharp and accurate lesion boundaries. Extensive experiments on the ISIC 2017 and ISIC 2018 benchmarks demonstrate state-of-the-art accuracy with strong efficiency, achieving 86.3% and 85.9% mIoU and 92.6% and 90.6% Dice, respectively, while requiring substantially fewer FLOPs than most competing CNN-based methods. These results highlight a favorable accuracy efficiency trade-off for high-resolution medical image segmentation.
Comments15 pages, 5 figures, accepted on International Conference on Cloud and Network Computing
