空间Omics驱动的交叉训练应用到基于图形的深度学习用于癌症病理学分析
Zarif L Azher1, Michael Fatemi, Yunrui Lu
1Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA, 2024zazher@tjhsst.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
将空间转录学与组织学集成,可以增强基于图形的深度学习,用于癌症病理学. 这种方法通过结合分子和形态数据来改善癌症分期,转移和生存的预测.
科学领域:
- 计算病理学计算病理学
- 生物信息学是一种生物信息学.
- 机器学习在瘤学中
背景情况:
- 基于图形的深度学习在癌症组织病理学中表现出色,通过分析整个幻灯片图像来预测结果.
- 当前的方法在幻灯片图中使用图像补丁嵌入作为节点属性.
- 空间奥米学,就像空间转录学一样,提供了丰富的分子数据,可以补充组织学成像.
研究的目的:
- 探索空间转录学数据与组织学成像相结合的实用性.
- 开发能够提取分子和组织学信息的深度学习模型.
- 通过整合多模式数据,增强癌症病理学中的基于图的学习任务.
主要方法:
- 利用一个对比的交叉模式预训练机制来整合空间转录和组织学数据.
- 生成深度学习模型来提取分子和组织学特征.
- 将这些模型应用于基于图形的学习任务,在组织病理幻灯片上.
主要成果:
- 提出的方法在癌症分期,淋巴结转移预测和生存预测方面表现得更好.
- 与现有方法相比,组织聚类分析也显示了改进.
- 整合空间奥米克数据显著改善了基于图形的病理学深度学习模型.
结论:
- 利用空间转录学数据与组织学成像一起,增强了病理学的深度学习模型.
- 这种多模式的方法显着有望改善癌症工作流程中的诊断和预后准确性.
- 挖掘空间omics数据是推动数字病理学深度学习的有价值策略.
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