对有限的肺炎X射线数据合成数据增强生成模型的批判性评估
Daniel Schaudt1, Christian Späte2, Reinhold von Schwerin3
1Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081 Ulm, Germany.
Bioengineering (Basel, Switzerland)
|December 23, 2023
概括
生成型模型创建合成医疗图像来增强有限的数据集. 虽然生成对抗网络 (GAN) 的图像质量更好,但这并不总是改善肺炎检测的诊断分类性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 深度学习模型需要大量的数据来进行医学图像分析.
- 医学成像方面的有限数据阻碍了培训有效的诊断模型.
- 人工图像生成是数据稀缺的一个潜在解决方案.
研究的目的:
- 为了比较合成医学图像创建的五种生成模型.
- 评估合成数据对肺炎分类任务的影响.
- 评估合成图像质量和分类性能之间的关系.
主要方法:
- 训练了五种生成模型,包括生成对抗网络 (GAN) 和扩散模型.
- 通过使用1082张胸部X射线图像对肺炎检测下游分类任务的评估合成数据.
- 使用各种指标评估图像质量,病理可信性和分类性能.
主要成果:
- 基于GAN的方法比扩散模型产生了更高质量的和更合理的合成图像.
- 改进的图像质量并不总是转化为更好的分类性能.
- 合成数据改善了特定类别的指标,特别是代表性不足的类别,重新平衡了数据集.
- 一个模型,DreamBooth,在整体准确度 (+0.52) 中略有改善.
结论:
- 生成模型可以在有限的医学成像场景中提高数据的可用性.
- 生成模型的选择会影响合成数据质量和下游任务性能.
- 在应用生成模型时需要仔细考虑,因为图像质量并不总是与更好的分类结果相关.
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