SAGE：适应性组织病理图像分割的形状自适应门控专家
SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation
AI总结：
SAGE通过动态专家路由框架提升异构视觉网络中细胞形态变化的适应性，实现高精度分割与稳健泛化。
摘要：
细胞大小和形状的显著差异仍然是计算机辅助癌症检测在吉像素全滑片图像中的主要障碍，由于细胞异质性。当前的CNN-Transformer混合模型使用静态计算图和固定路由，导致额外计算并难以适应输入变化。我们提出形状自适应门控专家（SAGE），一种输入自适应框架，通过双路径设计和层次门控以及形状适应枢纽（SA-Hub）将静态骨干网络重新配置为动态路由专家架构。SAGE以ConvNeXt和Vision Transformer UNet（SAGE-ConvNeXt+ViT-UNet）实现，其在EBHI上达到95.23%的Dice分数，在GlaS Test A和Test B上分别达到92.78%和91.42%的DSC分数，并在DigestPath上达到91.26%的DSC分数，同时在分布偏移下表现出稳健的泛化能力，通过自适应平衡局部细化和全局上下文。SAGE建立了可扩展的动态专家路由基础，从而促进灵活的视觉推理。项目页面：https://oxyzgiahuy.github.io/sage/
英文摘要：
The significant variability in cell size and shape continues to pose a major obstacle in computer-assisted cancer detection on gigapixel Whole Slide Images (WSIs), due to cellular heterogeneity. Current CNN-Transformer hybrids use static computation graphs with fixed routing. This leads to extra computation and makes it harder to adapt to changes in input. We propose Shape-Adapting Gated Experts (SAGE), an input-adaptive framework that enables dynamic expert routing in heterogeneous visual networks. SAGE reconfigures static backbones into dynamically routed expert architectures via a dual-path design with hierarchical gating and a Shape-Adapting Hub (SA-Hub) that harmonizes feature representations across convolutional and transformer modules. Embodied as SAGE with ConvNeXt and Vision Transformer UNet (SAGE-ConvNeXt+ViT-UNet), our model achieves a Dice score of 95.23% on EBHI, DSC scores of 92.78% and 91.42% on GlaS Test A and Test B, respectively, and 91.26% DSC at the WSI level on DigestPath, while exhibiting robust generalization under distribution shifts by adaptively balancing local refinement and global context. SAGE establishes a scalable foundation for dynamic expert routing in visual networks, thereby facilitating flexible visual reasoning. Project page: https://oxyzgiahuy.github.io/sage/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
