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MedCLIP:从未配对的医学图像和文本中进行对比学习

Zifeng Wang¹, Zhenbang Wu¹, Dinesh Agarwal^1,2

¹Department of Computer Science, University of Illinois Urbana-Champaign.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

|August 15, 2024

概括

通过将图像和文本脱而出,MedCLIP增强了医疗视觉-文本学习,大大减少了虚假负面. 这种方法以更少的数据实现了最先进的结果,改善了零射击预测和检索.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
计算机视觉计算机视觉

背景情况:

像CLIP这样的对比学习方法在一般视觉-文本任务中表现出色.
医疗数据集较小,在对比学习中容易出现错误负面结果.
现有的方法难以应对医疗多式联络数据的独特挑战.

研究的目的:

为医疗成像开发一个具有成本效益和可扩展性的多式联络对比学习框架.
为了解决医学视觉-文本对比学习中虚假负面的问题.
为了提高下游任务医疗图像-文本表示学习的性能.

主要方法:

将图像和文本脱,以指数级增加可用的培训数据.
实施语义匹配损失,利用医学知识来缓解虚假负面.
使用一个名为MedCLIP的新框架进行预培训.

主要成果:

在零射击预测,监督分类和图像文本检索方面,MedCLIP显著超过了最先进的方法.
与使用≈200K数据的基线相比,仅使用20K预训练样本实现了卓越的性能.
在医学多式模式学习中证明了脱和语义匹配损失的有效性.

结论:

MedCLIP提供了一个简单但非常有效的框架,用于医学视觉-文本对比学习.
提出的方法成功地解决了医疗领域的数据稀缺性和虚假负面问题.
MedCLIP为有限数据的医疗多式联络表征预培训设定了一个新的基准.

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