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精确瘤学的视觉语言基础模型

Jinxi Xiang1, Xiyue Wang1, Xiaoming Zhang2

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概括

一个新的AI模型,MUSK,整合了病理图像和临床文本,以改善癌症诊断和治疗预测. 这种多模式方法利用大量数据集来加强临床决策.

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科学领域:

  • 医学中的人工智能
  • 计算病理学
  • 医疗信息学

背景情况:

  • 临床决策依赖于整合各种数据,如临床笔记和病理学.
  • 开发有效的人工智能 (AI) 模型是具有挑战性的,因为没有足够的多模式临床数据集.
  • 现有的AI方法难以充分利用成像和文本临床数据中的互补信息.

研究的目的:

  • 开发一种新的视觉语言基础模型,具有统一的maSKed建模 (MUSK) 的多式变压器,能够集成大规模的,未标记的,未配对的病理图像和文本数据.
  • 在广泛的病理图像和文本数据集上预先训练MUSK,以便有效地对视觉和语言特征进行调整.
  • 评估MUSK在广泛的临床应用中所表现的效果,并且需要很少或没有进一步的培训.

主要方法:

  • 开发了MUSK视觉语言基础模型,使用统一的面具建模来预训练5000万个病理图像和10亿个文本令牌.
  • 此外还预先训练了100万个病理图像-文本对来对准视觉和语言模式.
  • 在23个补丁级和幻灯片级的基准测试中测试了MUSK,包括检索,视觉问题回答,分类和结果预测任务.

主要成果:

  • 在23个基准测试中,MUSK在图像到文本和文本到图像检索,视觉问题答案和图像分类方面表现出色.
  • 在分子生物标志物预测和结果预测方面取得了强有力的结果,包括黑色素瘤复发,泛癌预后以及肺癌和胃食道癌症的免疫治疗反应.
  • 有效地整合了病理图像和临床报告的信息,展示了其改善癌症诊断和治疗的潜力.

结论:

  • 预先训练的视觉语言基础模型MUSK成功地整合了多模式病理学数据 (图像和文本),用于高级临床应用.
  • 通过更好的结果预测,该模型在提高癌症治疗的诊断准确性和精度方面具有显著的前景.
  • 马斯克能够利用大规模的,未标记的,未配对的数据,这代表了AI在临床决策中的重大进步.