ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation

arXiv:2609.31160 · cs.CV, cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Embeddings — 19× in this paper · explained in 2 episodes
• Representation Learning — 10× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Loss Function — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Segment Anything Model (SAM) — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• While the Segment Anything Model — 1× in this paper
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• SAM-Med2D
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
• MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day
• Efficient MedSAMs: Segment Anything in Medical Images on Laptop
• DINOv2: Learning Robust Visual Features without Supervision
• Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

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