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

arXiv:2511.18493 · eess.IV, cs.AI, cs.CV · Submitted 2025-11-23 · Read on arXiv

eess.IV, cs.AI, cs.CV

Submitted: 2025-11-23

Updated: 2026-09-06

Comments: Accepted to CVPR 2026 (Findings Track). Project Page: https://oxyzgiahuy.github.io/sage/

Project page: https://oxyzgiahuy.github.io/sage

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 51× in this paper · explained in 2 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Shape-Adapting Gated Experts — 6× in this paper
• Aggregation — 5× in this paper · explained in 1 episode
• Mixture of Experts — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Semantic Affinity Routing — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• U-net architecture — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Domain Shift — 2× in this paper · explained in 4 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• alpha — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptive Multimodal Fusion — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Baseline Comparison and State-of-the-Art — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Dice Similarity Coefficient — 1× in this paper
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Extended Biopsy Histopathological Image — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective

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