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在视网膜成像中使用多模式表示学习,使用自我监督学习来增强临床预测.

Emese Sükei1, Elisabeth Rumetshofer2, Niklas Schmidinger2

  • 1OPTIMA Lab, Department of of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. emese.suekei@meduniwien.ac.at.

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使用多模式视网膜成像的自我监督学习创造了可转移的AI. 这种方法只使用 fundus 图像就能进行准确的预测,减少对光学连贯性断层扫描 (OCT) 的依赖.

关键词:
形成对比的预训练.多模式成像技术多模式成像技术预测建模的预测建模.代表性的学习学习.视网膜成像 视网膜成像

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 自主监督学习 (SSL) 对于医学成像中的可泛化AI至关重要.
  • 在多模数据上的对比表示学习产生可转移的特征.
  • 眼科提供了丰富的多模式视网膜成像数据 (2D fundus,3D OCT).

研究的目的:

  • 介绍一个新的多模式对比学习管道,用于关节视网膜成像表示.
  • 评估下游任务的学习表征的可转移性和通用性.
  • 评估使用低成本 fundus成像而不是OCT的影响.

主要方法:

  • 开发了一个多模模式对比学习框架,用于2D fundus和3D OCT扫描.
  • 在153,306对视网膜扫描对上进行了自我监督的预训.
  • 对临床预测任务的三个独立的外部数据集验证了框架.

主要成果:

  • 预培训框架产生了有效的检索系统和编码器.
  • 学习的表征在各种下游任务中得到了很好的概括.
  • 用 fundus 图像取代 OCT 保持了显著的预测能力.

结论:

  • 多模式对比学习有效地为视网膜成像创建联合表示.
  • 这种方法产生了可用于各种眼科应用的可转移特征.
  • 仅靠 Fundus 成像就足以完成某些预测任务,提供了一个具有成本效益的替代方案.