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.
@article{arxiv.2511.18493,
title = {SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation},
author = {Gia Huy Thai and Hoang-Nguyen Vu and Anh-Minh Phan and Quang-Thinh Ly and Tram Dinh and Thi-Ngoc-Truc Nguyen and Nhat Ho},
journal= {arXiv preprint arXiv:2511.18493},
year = {2026}
}