论文标题
用于增强显微镜的全球体素变压器网络
Global Voxel Transformer Networks for Augmented Microscopy
论文作者
论文摘要
深度学习的进步在增强显微镜上取得了显着成功，使我们能够在不使用昂贵的显微镜硬件和样品制备技术的情况下获得高质量的显微镜图像。但是，当前用于增强显微镜的深度学习模型主要是基于U-NET的神经网络，因此共享某些限制性能的缺点。在这项工作中，我们介绍了全球素素变压器网络（GVTNETS），这是一种用于增强显微镜的先进深度学习工具，它克服了当前基于U-NET的模型的固有局限性并实现了改善的性能。 GVTNET是建立在全球素体变压器运营商（GVTOS）上的，该操作员能够汇总全球信息，而不是像卷积这样的本地运营商。我们在现有数据集上应用所提出的方法，以在各种设置下进行三个不同的增强显微镜任务。与以前的基于U-NET的方法相比，该性能明显优越。
Advances in deep learning have led to remarkable success in augmented microscopy, enabling us to obtain high-quality microscope images without using expensive microscopy hardware and sample preparation techniques. However, current deep learning models for augmented microscopy are mostly U-Net based neural networks, thus sharing certain drawbacks that limit the performance. In this work, we introduce global voxel transformer networks (GVTNets), an advanced deep learning tool for augmented microscopy that overcomes intrinsic limitations of the current U-Net based models and achieves improved performance. GVTNets are built on global voxel transformer operators (GVTOs), which are able to aggregate global information, as opposed to local operators like convolutions. We apply the proposed methods on existing datasets for three different augmented microscopy tasks under various settings. The performance is significantly and consistently better than previous U-Net based approaches.
