MedVision: Benchmarking Quantitative Medical Image Analysis

arXiv:2511.18676 · cs.CV, cs.AI · Submitted 2025-11-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-11-24

Updated: 2026-08-29

Project page: https://medvision-vlm.github.io

Terminology

• Benchmark — 32× in this paper · explained in 2 episodes
• Fine-Tuning — 14× in this paper · explained in 5 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmarking — 7× in this paper · explained in 3 episodes
• F1 Score — 6× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Success Rate — 4× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• GRPO — 3× in this paper · explained in 6 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Qwen2.5-VL Technical Report
• CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats
• HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
• SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
• PathVQA: 30000+ Questions for Medical Visual Question Answering
• The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
• The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
• Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
• LLaVA-OneVision: Easy Visual Task Transfer
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
• HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
• PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
• SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
• MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
• MedGemma Technical Report
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Gemma 3 Technical Report

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