用于数据增强以改进结肠镜病变分类的GAN反演
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification
AI总结：
针对医学影像标注数据匮乏问题，提出基于GAN反演的数据增强方法，通过潜在空间操纵、模态转换与图像插值生成合成图像，使结肠镜息肉分类性能提升最高达6%。
摘要：
将深度学习应用于医学影像的一个主要挑战是标注数据的匮乏。本研究表明，由生成对抗网络（GAN）反演生成的合成结肠镜图像可作为训练数据，以提升深度学习模型的病变分类性能。该方法将具有相同标签的图像对反演至语义丰富且解耦的潜在空间，并操纵潜在表示以生成具有相同标签的新合成图像。我们执行了白光与窄带成像（NBI）之间的图像模态转换（风格迁移）。我们还通过在原始训练图像之间进行插值，生成了逼真的合成病变图像，以增加训练数据集中病变形状的多样性。我们表明，这些方法无需重新训练多个生成模型，即可优于对比性的结肠镜数据增强技术。该方法还利用了可能并非为特定结肠镜下游任务设计的数据集信息。例如，将肠道准备分级数据集用于息肉分类任务。我们的实验表明，该方法能执行多种结肠镜数据增强，使下游息肉分类性能优于基线及对比方法，提升最高达6%。
英文摘要：
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study demonstrates that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve the lesion classification performance of deep learning models. This approach inverts pairs of images with the same label to a semantically rich & disentangled latent space and manipulates latent representations to produce new synthetic images with the same label. We perform image modality translation (style transfer) between white light and narrowband imaging (NBI). We also generate realistic-looking synthetic lesion images by interpolating between original training images to increase the variety of lesion shapes in the training dataset. We show that these approaches outperform comparative colonoscopy data augmentation techniques without the need to re-train multiple generative models. This approach also leverages information from datasets that may not have been designed for the specific colonoscopy downstream task. E.g. using a bowel prep grading dataset for a polyp classification task. Our experiments show this approach can perform multiple colonoscopy data augmentations, which improve the downstream polyp classification performance over baseline and comparison methods by up to 6%.
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