镜像:医疗图像文本预训练,以应对杂环境的强度
Pujin Cheng1, Yijin Huang2, Li Lin3
1Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
Medical image analysis
|December 16, 2025
概括
MIRAGE通过解决噪音数据,以最佳的传输对比损失和自适应梯度平衡来增强医疗图像-文本预训练. 这种新的框架提高了对具有挑战性的医疗数据集的模型性能.
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:
- 对比视觉语言预训练 (VLP) 模型在大型一般数据集上表现出色.
- 由于高的数据收集和注释成本,医疗数据集往往是小的和杂的.
- 现有的方法与杂的医疗数据作斗争,导致过度匹配和糟糕的表示.
研究的目的:
- 推出MIRAGE,一个强大的医疗图像文本预培训框架.
- 在杂的医疗数据集中,解决不匹配的假阳性和语义相关的假阴性所带来的挑战.
- 提高医学领域的VLP模型的性能.
主要方法:
- 开发了MIRAGE,这是医疗图像文本预培训的新框架.
- 引入了基于运输的最佳对比损失,以识别和减轻噪音样本.
- 实施了适应性梯度平衡策略,以减少噪音梯度的影响.
主要成果:
- 在6个任务和14个数据集中,MIRAGE表现出卓越的性能.
- 该框架显著超过了现有的最先进的方法.
- 分析证实了每个组件在处理杂合成数据方面的有效性.
结论:
- MIRAGE提供了一个强大的解决方案,用于使用噪音数据进行医学图像-文本预训练.
- 建议的最佳运输损失和梯度平衡是克服数据限制的关键.
- 这一框架推动了VLP在医疗AI中的应用.
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