放射学报告改善了从放射图片中学到的视觉表现.
Haoxu Huang1, Samyak Rawlekar2, Sumit Chopra3
1Courant Institute of Mathematical Sciences, New York University, USA.
概括
多模式学习,结合视觉和文本数据从放射学,增强胸部X射线图分类. 联合学习策略显得有前途,特别是在有限的标记数据下,改进视觉表示.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 人类的感知整合了多模式的刺激 (声学,语言,视觉).
- 多模式学习框架对于自然数据集是有效的,但在生物医学中不太被探索.
- 放射学数据 (图像和报告) 提供了丰富的非结构化的多模式信息.
研究的目的:
- 适应和评估生物医学数据的多模式和自我监督的学习框架,特别是胸部X射线图分类.
- 为了比较多模式学习,自我监督学习和联合学习策略在这个领域的视觉表现的有效性.
主要方法:
- 从放射图像和报告中利用非结构化的多模式数据.
- 实施和比较多模式学习,自我监督学习和组合 (联合学习).
- 评价下游胸部X射线扫描分类任务的性能,特别是有限的标记数据 (1%和10%).
主要成果:
- 使用多模式和自我监督模型的联合学习在有限的标记数据设置 (1%和10%) 中超过了自我监督学习.
- 在这些条件下,联合学习可以与多模式学习相提并论.
- 多模式学习在分布之外的数据集上表现出更大的稳定性.
结论:
- 联合学习策略有效地增强了胸部X光学分类的视觉表示,特别是当标记数据稀缺时.
- 多模式学习提供了稳定性,使其成为生物医学应用的有价值的方法.
- 该研究提供了对优化医学成像分析学习策略的见解.
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