ViT-DAE：用于组织病理学图像分析的Transformer驱动扩散自编码器
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
AI总结：
本文提出ViT-DAE，首次将视觉Transformer引入扩散自编码器用于组织病理学图像合成，在三个公开数据集上生成逼真图像的效果优于GAN和普通DAE方法。
摘要：
近年来，生成式AI因其能够合成与原始数据源高度相似的数据而备受关注。尽管生成对抗网络（GANs）为组织病理学图像分析提供了创新方法，但它们存在模式崩溃和判别器过拟合等局限性。近期，去噪扩散模型在计算机视觉中展现出可喜的结果。这些模型在训练期间表现出卓越的稳定性、更好的分布覆盖率，并生成高质量多样化图像。此外，它们对噪声和扰动具有高度鲁棒性，非常适合用于数字病理学，因为该领域的图像通常包含伪影并表现出显著的染色差异。在本文中，我们提出了一种名为ViT-DAE的新方法，该方法将视觉Transformer（ViT）和扩散自编码器集成用于高质量组织病理学图像合成。这标志着首次将ViT引入计算病理学中的扩散自编码器，使模型能够更好地捕捉组织病理学图像复杂且精细的细节。我们在三个公开数据集上证明了ViT-DAE的有效性。我们的方法在生成逼真图像方面优于近期基于GAN和普通DAE的方法。
英文摘要：
Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial Networks (GANs) have provided innovative approaches for histopathological image analysis, they suffer from limitations such as mode collapse and overfitting in discriminator. Recently, Denoising Diffusion models have demonstrated promising results in computer vision. These models exhibit superior stability during training, better distribution coverage, and produce high-quality diverse images. Additionally, they display a high degree of resilience to noise and perturbations, making them well-suited for use in digital pathology, where images commonly contain artifacts and exhibit significant variations in staining. In this paper, we present a novel approach, namely ViT-DAE, which integrates vision transformers (ViT) and diffusion autoencoders for high-quality histopathology image synthesis. This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images. We demonstrate the effectiveness of ViT-DAE on three publicly available datasets. Our approach outperforms recent GAN-based and vanilla DAE methods in generating realistic images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
