建立高性能医疗基础模型的数据有效策略
Yuqi Sun1, Weimin Tan1, Zhuoyao Gu1
1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Nature biomedical engineering
|March 5, 2025
概括
合成数据生成增强了医学基础模型. 利用合成视网膜图像,研究人员用更少的现实数据构建了一个高性能模型,提高了糖尿病视网膜病变分级等任务的效率和概括性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 为基础模型收集大型医疗数据集是昂贵的,耗时的,并引发隐私问题.
- 现有的医学基础模型需要大量的现实数据进行预训练.
研究的目的:
- 研究合成数据在训练高性能医学基础模型中的有效性.
- 开发一种数据效率高的方法,用于预训练医学基础模型.
主要方法:
- 预先训练了一种视网膜基础模型,使用大约100万个合成视网膜图像.
- 将合成数据训练模型与在真实世界数据 (RETFound) 上训练的模型进行比较,仅使用了16.7%的真实图像.
- 在九个公共数据集和四个诊断任务中评估模型性能.
- 通过在胸部X射线图像上构建结核病检测分类器来验证方法.
主要成果:
- 数据效率模型在多个数据集和任务中实现了与RETFound可比或优于RETFound的性能.
- 对于糖尿病视网膜病变的分级,该模型与RETFound相比,仅使用了40%的专家注释的数据.
- 通过成功培训结核病检测分类器,证明了通用性.
结论:
- 通过疾病标签调节生成的合成数据可以有效地训练高性能医疗基础模型.
- 这种数据效率高的策略提高了模型性能和概括性,同时减轻了数据采集的挑战.
- 以文本为条件的合成数据生成对推进医疗人工智能的前景充满希望.
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