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Muhammad Usman Akbar^1,2, Måns Larsson³, Ida Blystad^2,4

¹Department of Biomedical Engineering, Linköping University, Linköping, Sweden.

Scientific data

|February 29, 2024

概括

由人工智能生成的合成医疗图像可以成为训练深度学习模型的真实数据的可行替代方案,在真实脑瘤成像数据集上训练模型的性能中达到80-90%.

科学领域:

医疗成像医学成像
人工智能的人工智能
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

为深度学习共享医学成像数据具有挑战性,因为隐私和法律约束.
生成型人工智能模型可以创建现实的合成医学图像,潜在地克服数据共享障碍.

研究的目的:

评估由生成对抗网络 (GAN) 生成的合成脑瘤图像和训练细分网络的扩散模型的有效性.
为了比较训练在合成与真实医学成像数据上的细分模型的性能.

主要方法:

四个GAN (渐进GAN,StyleGAN 1-3) 和一个扩散模型被用于生成合成脑图像.
两个细分网络,U-Net和Swin变压器,在真实和合成数据集上进行了训练,用于大脑瘤细分.
绩效通过使用子得分来评估.

主要成果:

在合成图像上训练的细分网络实现了在真实图像上训练的对象的80-90%的性能.
扩散模型显示了小型原始数据集的潜在记忆问题.
GAN和扩散模型证明了生成培训数据的可行性.

结论:

合成医疗图像是人工智能模型培训中数据共享的一个有希望的替代方案.
需要进一步的研究来解决局限性问题,特别是在扩散模型记忆方面.
生成的模型和数据可在 AIDA 数据中心上获得.

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