Global Voxel Transformer Networks for Augmented Microscopy
This work addresses performance limitations in augmented microscopy, which is important for researchers and practitioners needing high-quality images without expensive hardware, though it appears incremental as it builds on existing transformer methods.
The authors tackled the problem of limited performance in augmented microscopy by introducing global voxel transformer networks (GVTNets), which achieved significantly and consistently better results than previous U-Net based approaches across three tasks.
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.