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在人类器官中对功能组织单元进行细分,使用社区驱动的可通用机器学习算法的开发.

Yashvardhan Jain1, Leah L Godwin2, Sripad Joshi2

  • 1Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA. yashjain@iu.edu.

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概括

机器学习模型被开发为人类参考地图集的人类组织学图像中自动细分解剖结构. 这个社区努力克服了组织变异,最好的模型将被集成到HuBMAP门户.

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科学领域:

  • 计算生物学和数字病理学
  • 生物医学成像中的人工智能

背景情况:

  • 创建一个全面的健康人体参考地图集需要自动细分不同生物图像来源的解剖结构.
  • 样品制备和组织类型的变化对准确的图像分析提出了重大挑战.

研究的目的:

  • 促进社区驱动的机器学习 (ML) 模型的开发,用于组织学图像中解剖结构的自动细分.
  • 建立一个强大的ML管道来处理大规模组织图像数据集,以支持人类参考图谱 (HRA) 的构建.

主要方法:

  • 机器学习算法开发竞赛"黑客人体"由人类生物分子地图 (HuBMAP) 和人类蛋白质地图 (HPA) 团队在Kaggle上举办.
  • 创建了一个数据集,包含880个组织学图像,其中有12,901个细分结构.
  • 参与团队 (1175) 使用颜色规范化和混合模型 (具有卷积神经网络的视觉变压器) 等技术来解决组织变异.

主要成果:

  • 该竞赛成功地让全球社区参与开发用于生物图像细分的先进ML模型.
  • 使用有效的策略,包括颜色规范化和组合模型架构,以克服数据集挑战.
  • 性能最好的模型在细分各种解剖结构方面表现出高精度.

结论:

  • 由社区驱动的开放科学方法可以加快生物医学研究中复杂的ML工具的开发.
  • 最好的ML模型将集成到HuBMAP门户中,使组织图像可用于HRA的可扩展处理.
  • 这个倡议推动了创建一个详细的人类参考地图的进程,这对于理解人类生物学和疾病至关重要.