用GAN生成脑肿瘤MRI图像，让合成数据能直接用于诊断模型训练。
Generative Adversarial Synthesis and Deep Feature Discrimination of Brain Tumor MRI Images
- 基于DC-GAN生成逼真脑肿瘤MRI图像，解决真实数据不足问题。
- 合成数据训练的CNN分类器性能接近真实数据，准确率相当。
- 适合医学影像数据稀缺场景，可为医疗AI提供可靠合成数据。
与传统方法相比，深度学习（DL）已成为计算机视觉任务的关键技术。在医学影像（如磁共振成像，MRI）领域，合成数据生成是深度学习的重要应用，尤其因原始MRI数据有限而显得尤为关键。生成逼真的医学图像极具挑战性。生成对抗网络（GAN）在创建合成医学图像方面表现优异。本文提出一种基于深度学习的方法，利用深度卷积生成对抗网络（DC-GAN）生成合成MRI数据，以应对数据不足的问题。同时，采用卷积神经网络（CNN）分类器对合成数据和真实MRI数据进行脑肿瘤分类。通过对比分类结果，验证了合成图像的质量与实用性：在真实与合成数据上分类性能相当，证明了GAN生成图像在下游任务中的有效性。
原文摘要 · Abstract (English)
Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in the field of medical imaging such as Magnetic Resonance Imaging (MRI). The need for this task since the original MRI data is limited. The generation of realistic medical images is completely difficult and challenging. Generative Adversarial Networks (GANs) are useful for creating synthetic medical images. In this paper, we propose a DL based methodology for creating synthetic MRI data using the Deep Convolutional Generative Adversarial Network (DC-GAN) to address the problem of limited data. We also employ a Convolutional Neural Network (CNN) classifier to classify the brain tumor using synthetic data and real MRI data. CNN is used to evaluate the quality and utility of the synthetic images. The classification result demonstrates comparable performance on real and synthetic images, which validates the effectiveness of GAN-generated images for downstream tasks.
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