路径VLM-Eval:评估开放视觉语言模型在他的病理学
Nauman Ullah Gilal1, Rachida Zegour1, Khaled Al-Thelaya2
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Journal of pathology informatics
|July 21, 2025
概括
这项研究使用PathMMU数据集对60多个视觉语言模型 (VLM) 进行基因病理图像的基准测试. Qwen2-VL-72B-Instruct展示了卓越的性能,在数字病理诊断中推进了AI.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 数字病理学数字病理学
背景情况:
- 视觉语言模型 (VLMs) 正在推进人工智能,但缺乏特定领域的评估.
- 数字病理学可以从VLM中获得很大的好处,用于组织学解释和诊断.
- 目前的VLM评估仅限于一般数据集,阻碍了专门的应用.
研究的目的:
- 以基因病理图像理解为基准,对最先进的VLM的性能进行比较.
- 通过使用特定领域数据集,提供对VLM的公正和无污染的评估.
- 确定数字病理学应用的高性能VLM,并指导未来的研究.
主要方法:
- 在PathMMU数据集中对60多个VLM进行了广泛的零射击评估.
- 使用VLMEvalKit进行标准化和公正的模型性能评估.
- 包括PubMed,SocialPath和EduContent等多元选择题 (MCQ) 的各种子集.
主要成果:
- 在所有PathMMU子集中,Qwen2-VL-72B-Instruct获得了最高的63.97%的平均得分.
- 在评估的VLM中观察到显著的性能差异.
- 该研究扩大了与先前文献相比,评估的VLM的范围.
结论:
- 综合评估为开发下一代数字病理学的VLM提供了宝贵的资源.
- Qwen2-VL-72B-Instruct显示出有很大的潜力,可以帮助病理学家进行诊断推理.
- 这些发现将促进人工智能驱动的基因病理图像分析和医疗保健服务的进步.
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