{
 "id": "jiyaoliufd/MedQ-Bench",
 "owner": "jiyaoliufd",
 "name": "MedQ-Bench",
 "downloads": 213,
 "likes": 3,
 "last_modified": "2026-10-31T20:35:02.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "image-text-to-text"
 ],
 "citation": "@inproceedings{liu-etal-2026-mllms,\n    title = \"Can {MLLM}s Perceive and Describe Medical Image Quality Beyond\n      Scores?\",\n    author = \"Liu, Jiyao and Wei, Jinjie and Qu, Wanying and Ma, Chenglong and Ning, Junzhi and Li, Yunheng and Chen, Ying and Zhang, Jiafan and Chen, Pengcheng and Gao, Xin and Hu, Ming and Wang, Xin and Gao, Shujian and Yang, Dingkang and Deng, Zhongying and Ye, Jin and Liu, Lihao and He, Junjun and Xu, Ningsheng\",\n    editor = \"Lee, Hung-Yi  and\n      Dev, Sunipa  and\n      Li, Jessy  and\n      Elliott, Desmond\",\n    booktitle = \"Proceedings of the 2026 Conference on Empirical Methods in\n      Natural Language Processing\",\n    month = oct,\n    year = \"2026\",\n    address = \"Budapest, Hungary\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2026.emnlp-main.1129/\",\n    doi = \"10.18653/2026.emnlp-main.1129\",\n    pages = \"25854--25877\",\n    ISBN = \"979-8-89176-459-0\",\n    abstract = \"Medical Image Quality Assessment (IQA) serves as a critical\n      safety gate for clinical AI, yet existing approaches rely on scalar\n      metrics that fail to capture the descriptive, expert-like evaluation\n      process central to clinical practice. We introduce MedQ-Bench, a\n      comprehensive benchmark for language-based medical IQA evaluation with\n      MLLMs, establishing a perception{--}description paradigm across 5\n      imaging modalities and over 40 quality attributes. The benchmark\n      comprises two complemen",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "5866185a6a1d258a7ed5a8060598253abfc1237e",
 "stated_evidence": "https://huggingface.co/datasets/jiyaoliufd/MedQ-Bench/blob/5866185a6a1d258a7ed5a8060598253abfc1237e/README.md",
 "file_evidence": "https://huggingface.co/datasets/jiyaoliufd/MedQ-Bench/tree/5866185a6a1d258a7ed5a8060598253abfc1237e",
 "card_nc": false
}