论文标题
使用无监督方法来解释医学图像的生成模型的潜在空间
Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods
论文作者
论文摘要
生成模型，例如生成对抗网络（GAN）和变异自动编码器（VAE）在医学图像分析中起着越来越重要的作用。这些模型的潜在空间通常显示出与人解剖图像变换相对应的语义上有意义的方向。但是，到目前为止，由于有监督数据的要求，他们对医疗图像的探索一直受到限制。无监督在gan潜在空间中可解释的方向的几种方法在自然图像上显示出有趣的结果。这项工作探讨了通过训练胸腔CT扫描的gan和vae将这些技术应用于医学图像的潜力，并使用一种无监督的方法在产生的潜在空间中发现可解释的方向。我们发现几个方向对应于非平凡的图像转化，例如旋转或乳房大小。此外，该说明表明，尽管仅显示2D数据，但生成模型捕获了3D结构。结果表明，无监督的方法发现甘恩斯的可解释方向概括为VAE，并可以应用于医学图像。这在医学图像分析中使用这些方法开辟了许多未来的工作。
Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) play an increasingly important role in medical image analysis. The latent spaces of these models often show semantically meaningful directions corresponding to human-interpretable image transformations. However, until now, their exploration for medical images has been limited due to the requirement of supervised data. Several methods for unsupervised discovery of interpretable directions in GAN latent spaces have shown interesting results on natural images. This work explores the potential of applying these techniques on medical images by training a GAN and a VAE on thoracic CT scans and using an unsupervised method to discover interpretable directions in the resulting latent space. We find several directions corresponding to non-trivial image transformations, such as rotation or breast size. Furthermore, the directions show that the generative models capture 3D structure despite being presented only with 2D data. The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images. This opens a wide array of future work using these methods in medical image analysis.
