# DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration

arXiv: https://arxiv.org/abs/2001.09865 · HTML: https://mlsanity.com/paper/2001.09865/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

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

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Mutual information neural estimation for unsupervised multi-modal registration of brain images](https://mlsanity.com/paper/2201.10305/) (2022)
- [Calibrating LiDAR and Camera using Semantic Mutual information](https://mlsanity.com/paper/2104.12023/) (2021)
- [Towards Positive Jacobian: Learn to Postprocess Diffeomorphic Image Registration with Matrix Exponential](https://mlsanity.com/paper/2202.00749/) (2022)

## Abstract (arXiv)

In this work, we present a novel unsupervised image registration algorithm. It is differentiable end-to-end and can be used for both multi-modal and mono-modal registration. This is done using mutual information (MI) as a metric. The novelty here is that rather than using traditional ways of approximating MI, we use a neural estimator called MINE and supplement it with matrix exponential for transformation matrix computation. This leads to improved results as compared to the standard algorithms available out-of-the-box in state-of-the-art image registration toolboxes.