用GAN生成脑肿瘤影像，少量合成数据就能让分类模型达到95%以上准确率。
Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
- 混合真实与GAN生成的MRI图像，按不同比例训练CNN分类器
- 仅用100张合成图像+900张真实图像，测试准确率达95.2%，各项指标超95%
- 合成数据过多会引入伪影，影响模型在真实场景的泛化能力
生成对抗网络（GAN）在扩充有限的医学影像数据集方面展现出潜力。本研究探讨了不同比例的GAN生成与真实脑肿瘤MRI图像对卷积神经网络（CNN）分类性能的影响。采用DCGAN生成合成图像，并以不同比例与真实图像混合，用于训练自定义CNN。模型在独立的真实测试集上进行评估。结果显示，即使主要使用合成数据，模型仍保持高敏感性和精确度。当仅加入少量合成数据（如900张真实图像配100张GAN图像）时，模型表现优异，测试准确率达到95.2%，精确率、召回率和F1分数均超过95%。然而，随着合成数据比例进一步增加，性能逐渐下降。研究表明，尽管GAN可用于扩充稀缺的真实数据，但过度依赖合成数据可能引入伪影，影响模型在真实世界中的泛化能力。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains high sensitivity and precision in tumor classification, even when trained predominantly on synthetic data. When only a small portion of GAN data was added, such as 900 real images and 100 GAN images, the model achieved excellent performance, with test accuracy reaching 95.2%, and precision, recall, and F1-score all exceeding 95%. However, as the proportion of GAN images increased further, performance gradually declined. This study suggests that while GANs are useful for augmenting limited datasets especially when real data is scarce, too much synthetic data can introduce artifacts that affect the model's ability to generalize to real world cases.
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