# DeepSim: Semantic similarity metrics for learned image registration

arXiv: https://arxiv.org/abs/2011.05735 · HTML: https://mlsanity.com/paper/2011.05735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Medical Image Registration: A Comprehensive Review](https://mlsanity.com/paper/2204.11341/) (2022)
- [MAD: Modality Agnostic Distance Measure for Image Registration](https://mlsanity.com/paper/2309.02875/) (2023)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [General Vision Encoder Features as Guidance in Medical Image Registration](https://mlsanity.com/paper/2407.13311/) (2024)

## Abstract (arXiv)

We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our semantic approach learns dataset-specific features that drive the optimization of a learning-based registration model. Comparing to existing unsupervised and supervised methods across multiple image modalities and applications, we achieve consistently high registration accuracy and faster convergence than state of the art, and the learned invariance to noise gives smoother transformations on low-quality images.