# Artificial Intelligence in Minimally Invasive Interventional Treatment

arXiv: https://arxiv.org/abs/2106.15306 · HTML: https://mlsanity.com/paper/2106.15306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Estimating Depth from RGB and Sparse Sensing](https://mlsanity.com/paper/1804.02771/) (2018)
- [Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks](https://mlsanity.com/paper/1808.04187/) (2018)

## Abstract (arXiv)

Minimally invasive image guided treatment procedures often employ advanced image processing algorithms. The recent developments of artificial intelligence algorithms harbor potential to further enhance this domain. In this article we explore several application areas within the minimally invasive treatment space and discuss the deployment of artificial intelligence within these areas.