Duplicate from jiyaoliufd/MedQ-Bench

Co-authored-by: jiyaoliu <jiyaoliufd@users.noreply.huggingface.co>

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README.md

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+---
+task_categories:
+- image-text-to-text
+license: apache-2.0
+language: en
+tags:
+- medical-imaging
+- image-quality-assessment
+- mllm
+- benchmark
+- multimodal
+---
+
+<div align="center">
+
+# MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
+
+_Bridging the gap between traditional medical IQA and human-like reasoning with Multi-modal Large Language Models_
+
+</div>
+
+> - **Project Page**: https://github.com/liujiyaoFDU/MedQBench
+> - **Code**: https://github.com/liujiyaoFDU/MedQBench
+> - **Paper**: https://arxiv.org/abs/2510.01691
+
+## Dataset Description
+
+MedQ-Bench is the first comprehensive benchmark for evaluating Medical Image Quality Assessment (IQA) capabilities of Multi-modal Large Language Models (MLLMs). Unlike traditional score-based IQA methods, MedQ-Bench introduces a perception-reasoning paradigm that mirrors clinicians' cognitive workflow for quality assessment.
+
+### Dataset Overview
+- **Total Samples**: 3,308 medical images
+- **Modalities**: 5 imaging types (CT, MRI, Histopathology, Endoscopy, Fundus Photography)
+- **Quality Attributes**: 40+ degradation types
+- **Tasks**: 2,600 perception queries + 708 reasoning assessments
+- **Sources**: Authentic clinical images, simulated degradations, AI-generated images
+
+### Tasks
+1. **MedQ-Perception**: Multiple-choice questions on fundamental visual quality attributes (Yes/No, What, How)
+2. **MedQ-Reasoning**: No-reference and comparison reasoning tasks with human-like quality assessment
+
+## Evaluation Results
+
+### Perception Task Performance (Test Set)
+
+| Model | Yes-or-No ↑ | What ↑ | How ↑ | Overall ↑ |
+|-------|-------------|--------|-------|-----------|
+| **GPT-5** | **82.26%** | **60.47%** | 58.28% | **68.97%** |
+| GPT-4o | 78.48% | 49.64% | 57.32% | 64.79% |
+| Grok-4 | 73.30% | 48.84% | **59.10%** | 63.14% |
+| Qwen2.5-VL-72B | 78.67% | 42.25% | 56.44% | 63.14% |
+| Gemini-2.5-Pro | 75.13% | 55.02% | 50.54% | 61.88% |
+| InternVL3-38B | 69.71% | 57.36% | 52.97% | 61.00% |
+| Claude-4-Sonnet | 71.51% | 46.51% | 54.60% | 60.23% |
+| InternVL3-8B | 72.04% | 47.67% | 52.97% | 60.08% |
+| Qwen2.5-VL-32B | 67.38% | 43.02% | 58.69% | 59.31% |
+| Mistral-Medium-3 | 65.95% | 48.84% | 52.97% | 57.70% |
+| MedGemma-27B | 67.03% | 48.06% | 50.72% | 57.16% |
+| Qwen2.5-VL-7B | 57.89% | 48.45% | 54.40% | 54.71% |
+| Lingshu-32B | 50.36% | 50.39% | 51.74% | 50.88% |
+| BiMediX2-8B | 44.98% | 27.52% | 27.81% | 35.10% |
+| Random Guess | 50.00% | 28.48% | 33.30% | 37.94% |
+
+### No-Reference Reasoning Task Performance (Test Set)
+
+| Model | Comp. ↑ | Prec. ↑ | Cons. ↑ | Qual. ↑ | Overall ↑ |
+|-------|---------|---------|---------|---------|-----------|
+| **GPT-5** | **1.195** | **1.118** | 1.837 | 1.529 | **5.679** |
+| GPT-4o | 1.009 | 1.027 | 1.878 | 1.407 | 5.321 |
+| Qwen2.5-VL-32B | 1.077 | 0.928 | **1.977** | 1.290 | 5.272 |
+| Grok-4 | 0.982 | 0.846 | 1.801 | 1.389 | 5.017 |
+| Gemini-2.5-Pro | 0.878 | 0.891 | 1.688 | **1.561** | 5.018 |
+| InternVL3-8B | 0.928 | 0.878 | 1.858 | 1.317 | 4.983 |
+| Qwen2.5-VL-72B | 0.905 | 0.860 | 1.896 | 1.321 | 4.982 |
+| InternVL3-38B | 0.964 | 0.824 | 1.860 | 1.317 | 4.965 |
+| Mistral-Medium-3 | 0.923 | 0.729 | 1.566 | 1.339 | 4.557 |
+| Claude-4-Sonnet | 0.742 | 0.633 | 1.778 | 1.376 | 4.529 |
+| Qwen2.5-VL-7B | 0.715 | 0.670 | 1.855 | 1.127 | 4.367 |
+| Lingshu-32B | 0.624 | 0.697 | 1.932 | 1.059 | 4.312 |
+| MedGemma-27B | 0.742 | 0.471 | 1.579 | 1.262 | 4.054 |
+| BiMediX2-8B | 0.376 | 0.394 | 0.281 | 0.670 | 1.721 |
+
+### Comparison Reasoning Task Performance (Test Set)
+
+| Model | Comp. ↑ | Prec. ↑ | Cons. ↑ | Qual. ↑ | Overall ↑ |
+|-------|---------|---------|---------|---------|-----------|
+| **GPT-5** | **1.293** | **1.556** | 1.925 | **1.564** | **6.338** |
+| GPT-4o | 1.105 | 1.414 | 1.632 | 1.562 | 5.713 |
+| Grok-4 | 1.150 | 1.233 | 1.820 | 1.459 | 5.662 |
+| Gemini-2.5-Pro | 1.053 | 1.233 | 1.774 | 1.534 | 5.594 |
+| InternVL3-8B | 0.985 | 1.278 | 1.797 | 1.474 | 5.534 |
+| Claude-4-Sonnet | 0.857 | 1.083 | **1.910** | 1.481 | 5.331 |
+| Mistral-Medium-3 | 0.872 | 1.203 | 1.827 | 1.338 | 5.240 |
+| InternVL3-38B | 1.075 | 1.083 | 1.571 | 1.414 | 5.143 |
+| Lingshu-32B | 0.729 | 1.015 | 1.586 | 1.323 | 4.653 |
+| Qwen2.5-VL-32B | 0.692 | 0.752 | 1.895 | 0.962 | 4.301 |
+| Qwen2.5-VL-7B | 0.714 | 0.902 | 1.316 | 1.143 | 4.075 |
+| Qwen2.5-VL-72B | 0.737 | 0.977 | 1.233 | 1.113 | 4.060 |
+| MedGemma-27B | 0.684 | 0.692 | 1.128 | 1.000 | 3.504 |
+| BiMediX2-8B | 0.474 | 0.549 | 0.639 | 0.511 | 2.173 |
+
+
+## Key Findings
+
+### Performance Hierarchy
+- **Closed-source frontier models** achieve highest performance (GPT-5 leads with 68.97% perception accuracy)
+- **Open-source models** show competitive results (Qwen2.5-VL-72B: 63.14%)
+- **Medical-specialized models** underperform expectations (best: MedGemma-27B at 57.16%)
+
+### Performance Gaps
+- **Human-AI gap**: Best model (GPT-5) trails human experts by 13.53% in perception tasks
+- **Fine-grained analysis**: Models struggle with subtle quality degradations (mild degradation detection: 56% avg accuracy)
+
+### Model Categories
+🟢 **General-purpose MLLMs**: Qwen2.5-VL, InternVL3
+🔵 **Medical-specialized**: BiMediX2, MedGemma, Lingshu
+🟠 **Commercial systems**: GPT-5, GPT-4o, Claude-4, Gemini-2.5-Pro, Grok-4, Mistral-Medium-3
+
+## Citation
+
+```bibtex
+@misc{liu2025medqbenchevaluatingexploringmedical,
+      title={MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs}, 
+      author={Jiyao Liu and Jinjie Wei and Wanying Qu and Chenglong Ma and Junzhi Ning and Yunheng Li and Ying Chen and Xinzhe Luo and Pengcheng Chen and Xin Gao and Ming Hu and Huihui Xu and Xin Wang and Shujian Gao and Dingkang Yang and Zhongying Deng and Jin Ye and Lihao Liu and Junjun He and Ningsheng Xu},
+      year={2025},
+      eprint={2510.01691},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2510.01691}, 
+}
+```

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