[2511.18493] SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2511.18493 (eess) [Submitted on 23 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v3)] Title:SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation Authors:Gia Huy Thai, Hoang-Nguyen Vu, Anh-Minh Phan, Quang-Thinh Ly, Tram Dinh, Thi-Ngoc-Truc Nguyen, Nhat Ho View a PDF of the paper titled SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation, by Gia Huy Thai and 5 other authors View PDF HTML (experimental) 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 ...