# dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

arXiv: https://arxiv.org/abs/1909.10995 · HTML: https://mlsanity.com/paper/1909.10995/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning](https://mlsanity.com/paper/2206.07050/) (2022)
- [A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction](https://mlsanity.com/paper/2207.01791/) (2022)
- [On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy](https://mlsanity.com/paper/2202.05267/) (2022)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)
- [Multi-scale MRI reconstruction via dilated ensemble networks](https://mlsanity.com/paper/2310.04705/) (2023)

## Abstract (arXiv)

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.