RAU: Reference-based Anatomical Understanding with Vision Language Models

arXiv:2509.22404 · cs.CV, cs.AI · Submitted 2025-09-26 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-09-26

Updated: 2026-09-08

Comments: ECCV 2026

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

Terminology

• Accuracy — 27× in this paper · explained in 1 episode
• Generalization — 16× in this paper · explained in 13 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Foundation Model — 10× in this paper · explained in 3 episodes
• GRPO — 8× in this paper · explained in 6 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Reward Function — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• nnU-Net — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Generalization to Out-of-Distribution — 1× in this paper
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen Technical Report
• R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest
• How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
• A Survey on In-context Learning
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
• ECHOPulse: ECG controlled echocardio-grams video generation
• Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
• DeepSeek-V3 Technical Report
• A Survey on Hallucination in Large Vision-Language Models
• SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data
• DINOv2: Learning Robust Visual Features without Supervision
• SAM 2: Segment Anything in Images and Videos
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
• Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
• Foundation Models in Medical Imaging: A Review and Outlook
• SVQA-R1: Reinforcing Spatial Reasoning in MLLMs via View-Consistent Reward Optimization
• SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

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