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精密瘤学的计算病理学:从特定任务模型到基础模型的演变.

Yuhao Wang1,2, Yunjie Gu1,2, Xueyuan Zhang3

  • 1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, Hefei 230026, China.

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概括

基础模型 (FMs) 正在通过超越特定任务的AI来彻底改变计算病理学. 这些先进的模型为各种临床任务提供可扩展的解决方案,包括罕见疾病和瘤学中的生物标志物发现.

关键词:
人工智能的人工智能是人工智能.计算病理学计算病理学深度学习是一种深度学习.基金会模型 基金会模型精确瘤学是一门精确的专业.

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科学领域:

  • 计算病理学计算病理学
  • 医学中的人工智能
  • 生物医学信息学是生物医学信息学.

背景情况:

  • 人工智能 (AI) 越来越多地融入临床病理学工作流程,提高准确性和效率.
  • 传统的人工智能模型需要针对特定任务进行广泛的标记数据集,限制罕见疾病的可扩展性和开放式识别.
  • 对可适应的人工智能解决方案的需求需要从特定任务模型转向更通用的基础模型 (FMs).

研究的目的:

  • 审查病理基础模型 (FMs) 的最新进展.
  • 根据它们的架构和功能 (图像,图像-文本,图像-基因) 来分类FM.
  • 探索FMs在精密瘤学的应用,挑战和机遇.

主要方法:

  • 关于病理基础模型的最新研究的文献综述.
  • 将FM分为三个主要类别:病理图像FM,病理图像-文本FM和病理图像-基因FM.
  • 分析每个FM类别的应用场景和功能,特别是在瘤学中.

主要成果:

  • 病理性FM已经成为一种强大的工具,分为图像,图像-文本和图像-基因类型.
  • 这些FM展示了各种功能和应用场景,增强了诊断,治疗,预后和生物标志物发现.
  • 在将FM应用于瘤学方面取得了重大进展,为精准医学铺平了道路.

结论:

  • 基础模型代表了计算病理学的范式转变,解决了特定任务方法的局限性.
  • 病理性FM为广泛的临床任务提供可扩展和适应的解决方案,包括罕见疾病分析.
  • 通过使更复杂的生物标志物发现和个性化治疗策略成为可能,FMS在推进精确瘤学方面具有巨大的潜力.