| --- |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| size_categories: |
| - n<1K |
| tags: |
| - medical |
| - multimodal |
| - evaluation |
| - rubric |
| - huatuogpt-vision-2 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: medmm_expert_rubric.json |
| --- |
| |
| <div align="center"> |
| <h1>MedMM-ExpertRubric</h1> |
| <a href="https://openreview.net/pdf?id=0BXKsjKlFJ">📄 Paper</a> | |
| <a href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision-2">💻 GitHub</a> |
| </div> |
|
|
| ## ⚡ Introduction |
|
|
| **MedMM-ExpertRubric** is an open-ended medical multimodal benchmark with **physician-verified rubrics**, released with **HuatuoGPT-Vision-2**. It contains **500 questions, 538 images, and 11,088 rubric items**. |
|
|
| - 👨⚕️ **Physician-verified.** Licensed physicians filter every rubric item, keeping only core image-understanding points. |
| - 🔬 **Fine-grained scoring.** About 22 weighted rubric items per question. Positive items reward key findings, and negative items penalize medical errors. |
| - 🩻 **Diverse and held-out.** Covers radiology, pathology, ultrasound, endoscopy, and more, with 32 multi-image questions. None of the cases is used in HuatuoGPT-Vision-2 training. |
| - 🤝 **Aligned with physicians.** Rankings agree with physician pairwise preference, making it a low-cost proxy for expert evaluation. |
|
|
| ## 📚 What's inside |
|
|
| | File | Content | |
| | --- | --- | |
| | `inference_inputs.json` | Question and images only. Use this as model input. | |
| | `medmm_expert_rubric.json` | Full records with rubrics, caption, modality, and anatomy. For scoring only. | |
|
|
| ## 🚀 Usage |
|
|
| Load the downloaded data: |
|
|
| ```python |
| import json |
| from pathlib import Path |
| from PIL import Image |
| |
| root = Path("/path/to/MedMM-ExpertRubric") |
| dataset = json.loads((root / "inference_inputs.json").read_text()) |
| case = dataset[0] |
| images = [Image.open(root / path) for path in case["images"]] |
| print(case["question"]) |
| ``` |
|
|
| Provide the question and images in order to the model. Use the rubrics for evaluation only. Do not include the caption in model input. |
|
|
| An LLM grader marks each rubric item as met or not met. The score is the sum of points for met items divided by the total positive points. We use GPT-4.1 in the paper, and other strong LLMs can also serve as the grader. |
|
|
| ## 📖 Citation |
|
|
| ```bibtex |
| @inproceedings{chen2026incentivizing, |
| title={Incentivizing Medical Vision Capabilities from Large-Scale Multimodal Pre-training}, |
| author={Chen, Junying and Cai, Zhenyang and Yang, Yunjin and Cai, Kunyuan and Wang, Rongsheng and Wang, Xidong and Yu, Geyang and Feng, Xiangyi and Wang, Benyou}, |
| booktitle={The Fortieth Annual Conference on Neural Information Processing Systems}, |
| year={2026} |
| } |
| ``` |
|
|
