路径基准:在补丁和整个幻灯片层面上推进病理图像理解的大型多式模式的基准
IEEE transactions on medical imaging
|July 2, 2025
概括
通过新的补丁和整个幻灯片图像 (WSI) 数据集,PathBench增强了病理学中大型多式模式 (LMM) 的验证. 它介绍了PatchVQA和WSIVQA,用于对LMM进行基准测试,并防止捷径学习.
科学领域:
- 数字病理学数字病理学
- 人工智能的人工智能是人工智能.
- 医学成像分析分析 医学成像分析
背景情况:
- 大型多式模式 (LMM) 在病理学任务中表现有希望,例如图像分类和描述.
- 病理学中的全幻灯片图像 (WSIs) 是千兆像素级的,需要在补丁级和WSI级进行评估.
- 现有的数据集可能无法充分评估LMM的能力,这可能导致捷径学习.
研究的目的:
- 引入PathBench,这是数字病理学中LMMs的全面验证框架.
- 开发新的数据集,PatchVQA和WSIVQA,用于强大的补丁级和WSI级LMM评估.
- 在这些新的病理学数据集上对当前最先进的LMM进行基准测试.
主要方法:
- 创建了PatchVQA,这是一个多选项视觉问题答案数据集,用于补丁级评估.
- 通过将基本模型补丁描述与医生报告相结合,生成了WSICap,一个WSI报告数据集.
- 开发了WSIVQA,这是WSI级的视觉问题答案数据集,用于多式模式验证.
主要成果:
- 评估显示,一些LMM在现有的补丁级数据集上表现出了快捷方式学习.
- 路径基准数据集提供了在补丁和WSI层面对LMM的更严格的评估.
- 基准测试证明了在拟议的数据集中最先进的LMM的性能.
结论:
- PathBench提供了一种精细的方法来验证数字病理学中的LMM.
- 新的数据集,PatchVQA和WSIVQA,对于推动LMM开发和防止不可靠的评估至关重要.
- 这项工作为病理学中的LMM性能建立了基准,为更准确的诊断工具铺平了道路.
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