结合真实和合成数据,克服多模式学习中的有限培训数据集
Niccolo Marini1, Zhaohui Liang1, Sivaramakrishnan Rajaraman1
1Division of Intramural Research, National Library of Medicine, National Institutes of Health Bethesda, MD, 290894, USA.
medRxiv : the preprint server for health sciences
|August 12, 2025
概括
这项研究引入了一种新的多式联络深度学习方法,用于皮肤病变的分类. 通过使用大型语言模型 (LLM) 合成文本数据,该方法增强了图像嵌入,以提高皮肤病学诊断准确度.
科学领域:
- 生物医学信息学是生物医学信息学.
- 医学中的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 生物医学数据通常是多式联通的,提供补充的患者见解.
- 多模式深度学习 (DL) 可以增强临床决策,但需要配对数据.
- 公共可用的生物医学数据集往往是单模式的,阻碍了多模式DL的开发.
研究的目的:
- 开发一种策略,用于创建皮肤病变数据的强大的多式模式表示.
- 为了应对公共皮肤病变数据集中有限的配对数据的挑战.
- 提高使用多模式DL的自动皮肤病变分类的性能.
主要方法:
- 设计了一种多模式架构,将图像嵌入与细粒度文本表示集成在一起.
- 使用大型语言模型 (LLM) 来从图像元数据中合成文字描述.
- 合成的文本数据与原始的皮肤病变图像相结合,用于模型训练.
主要成果:
- 拟议的多模式表示在皮肤病变分类方面明显优于单模式方法.
- 在9个不同的内部和外部数据集中实现了卓越的性能.
- 合成文本数据的整合增强了模型的稳定性.
结论:
- 开发的策略有效地利用合成数据来创建用于皮肤病变分析的强大的多式模式表示.
- 这种方法克服了单模数据集的局限性,并推动了多模DL在皮肤病学的应用.
- 这些发现表明了改善临床实践中自动诊断工具的有希望的方向.
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