# Global Voxel Transformer Networks for Augmented Microscopy

arXiv: https://arxiv.org/abs/2008.02340 · HTML: https://mlsanity.com/paper/2008.02340/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy](https://mlsanity.com/paper/2101.04266/) (2021)
- [A Transformer-based Network for Deformable Medical Image Registration](https://mlsanity.com/paper/2202.12104/) (2022)
- [Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis](https://mlsanity.com/paper/2407.19512/) (2024)
- [Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies](https://mlsanity.com/paper/2309.15132/) (2023)
- [Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling](https://mlsanity.com/paper/2507.05383/) (2025)

## Abstract (arXiv)

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.