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使用训练损失的回归斜率来优化胸部X射线生成在深度卷积生成对抗网络
Chih-Hsiung Chen1, Kuang-Yu Hsieh1, Kuo-En Huang1
1Department of Critical Care Medicine, Mennonite Christian Hospital, Hualien, TWN.
生成对抗网络 (GAN) 在医学图像生成中可能会失败. 分析训练损失有助于确定最佳的停止点,以确保有意义的图像生成用于数据增强.
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:
- 生成对抗网络 (GAN) 广泛用于医学图像生成和数据增强.
- GAN训练可能导致无法产生有意义的图像,这是一个很少讨论的问题.
- 这种失败可以在医学AI模型培训中引入偏见,并且需要大量的计算资源.
研究的目的:
- 为了研究胸部X射线生成中的深卷积GAN的故障模式.
- 提出一种方法来确定最佳的培训终点,以确保有意义的图像生成.
- 为了应对GAN产生无法用于医疗数据增强的图像的挑战.
主要方法:
- 利用深度卷积GAN用于胸部X射线图像生成.
- 分析了训练损失历史,以确定与成功和失败的图像生成相关的模式.
- 开发了一种基于稳定损失回归线斜率的方法,以确定最佳的训练终止.
主要成果:
- 确定了三个典型的培训结果:两个成功的和一个失败的生成场景.
- 观察到总损失的回归线在失败的场景中倾向于缓慢分离.
- 确定稳定损失段内回归线的斜率可靠地指示最佳训练终结点.
结论:
- 拟议的方法有效地确定了GAN在医学图像生成中的最佳培训点.
- 这种方法减轻了产生偏见或无法用于数据增强的图像的风险.
- 优化GAN训练终止对于可靠的医学图像合成至关重要.
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