SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation
Quick summary
arXiv:2511.18493v5 Announce Type: replace-cross 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 in
Key takeaways
- arXiv:2511.18493v5 Announce Type: replace-cross 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.
Why it matters
“SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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