{"ID":2838034,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18493","arxiv_id":"2511.18493","title":"SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation","abstract":"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, 92.78\\%/91.42\\% DSC on GlaS Test A/Test B, 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.","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2511.18493","url_pdf":"https://arxiv.org/pdf/2511.18493v3","authors":"[\"Gia Huy Thai\",\"Hoang-Nguyen Vu\",\"Anh-Minh Phan\",\"Quang-Thinh Ly\",\"Tram Dinh\",\"Thi-Ngoc-Truc Nguyen\",\"Nhat Ho\"]","published":"2025-11-23T15:25:36Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}