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多中心组织学图像集成和多尺度深度学习用于机器学习启用的儿科肉瘤分类.

Adam Thiesen1,2, Sergii Domanskyi1, Ali Foroughi Pour1

  • 1The Jackson Laboratory for Genomic Medicine.

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此摘要是机器生成的。

深度学习模型从数字组织学幻灯片准确地分类罕见的儿科肉瘤亚型,提高诊断速度和可访问性. 这种计算方法提高了精度,并减少了诊断这些具有挑战性的儿童癌症的变异性.

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科学领域:

  • 计算病理学计算病理学
  • 人工智能在瘤学中的应用
  • 数字组织病理学 数字组织病理学

背景情况:

  • 儿科肉瘤是罕见的和多样化的,这给诊断带来了挑战,需要专门的专业知识和昂贵的遗传测试.
  • 现有的诊断方法可能受到观察者之间的变异性和可访问性问题的限制.

研究的目的:

  • 开发和验证深度学习计算管道,以使用数字化组织学幻灯片准确对儿科肉瘤亚型进行分类.
  • 通过创建一个可通用和高效的分类系统来克服诊断障碍.

主要方法:

  • 使用了来自多个机构的867个全幻灯片图像 (WSI) 的统一数据集.
  • 评估了各种深度学习架构,包括卷积神经网络 (CNN) 和视觉转换器 (ViT).
  • 输入参数如尺寸和分辨率被优化用于使用基于SAMPLER的WSI表示的特征提取.

主要成果:

  • 先进的视觉变压器 (ViT) 基础模型 (UNI,CONCH) 展示了卓越的性能.
  • 这些模型实现了高AUC值:0.969±0.026用于区分拉布多米索尔科马 (RMS) 和非拉布多米索尔科马 (NRSTS),0.961±0.021用于区分RMS亚型.
  • 一个两阶段的管道确定了欧文肉瘤,AUC为0.929.
  • 基于SAMPLER的分类器比传统的变压器架构轻量化得多,训练速度更快.

结论:

  • 数字组织病理学与严格的图像协调相结合,为儿科肉瘤分类提供了强大的解决方案.
  • 开发的模型减少了观察者之间的变化,提高了诊断精度.
  • 这种方法有可能改善全球对诊断的可访问性,从而使儿科肉瘤患者的治疗计划更快.