# Information-based Disentangled Representation Learning for Unsupervised MR Harmonization

arXiv: https://arxiv.org/abs/2103.13283 · HTML: https://mlsanity.com/paper/2103.13283/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation](https://mlsanity.com/paper/1809.01361/) (2018)
- [Compressing Neural Networks using the Variational Information Bottleneck](https://mlsanity.com/paper/1802.10399/) (2018)
- [Test-time Unsupervised Domain Adaptation](https://mlsanity.com/paper/2010.01926/) (2020)
- [Self domain adapted network](https://mlsanity.com/paper/2007.03162/) (2020)
- [Unsupervised Multi-Domain Multimodal Image-to-Image Translation with Explicit Domain-Constrained Disentanglement](https://mlsanity.com/paper/1911.00622/) (2019)
- [Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis](https://mlsanity.com/paper/2103.02767/) (2021)

## Built on by (most cited first, 16 of 16)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [HACA3: A Unified Approach for Multi-site MR Image Harmonization](https://mlsanity.com/paper/2212.06065/) (2022)
- [ImUnity: a generalizable VAE-GAN solution for multicenter MR image harmonization](https://mlsanity.com/paper/2109.06756/) (2021)
- [Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms](https://mlsanity.com/paper/2110.00041/) (2021)
- [Disentangling A Single MR Modality](https://mlsanity.com/paper/2205.04982/) (2022)
- [Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows](https://mlsanity.com/paper/2301.11551/) (2023)
- [MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent](https://mlsanity.com/paper/2203.04317/) (2022)
- [IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images](https://mlsanity.com/paper/2402.03227/) (2024)
- [Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization](https://mlsanity.com/paper/2407.15717/) (2024)
- [AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution](https://mlsanity.com/paper/2312.04385/) (2023)
- [Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation](https://mlsanity.com/paper/2305.14589/) (2023)
- [Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors](https://mlsanity.com/paper/2211.07820/) (2022)
- [Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration](https://mlsanity.com/paper/2401.02141/) (2024)
- [Image Denoising via Style Disentanglement](https://mlsanity.com/paper/2309.14755/) (2023)
- [A Target-Free Harmonization Method for MRI](https://mlsanity.com/paper/2605.01282/) (2026)
- [Feynman Kac Reweighted Schr"odinger Bridge Matching for Surface-Based Tau PET Harmonization](https://mlsanity.com/paper/2606.17420/) (2026)

## Abstract (arXiv)

Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate for contrast variation in MR images. Current harmonization approaches either require cross-site traveling subjects for supervised training or heavily rely on site-specific harmonization models to encourage harmonization accuracy. These requirements potentially limit the application of current harmonization methods in large-scale multi-site studies. In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory. CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. Our model is also able to adapt itself to harmonize MR images from a new site with fine tuning solely on images from the new site. Both qualitative and quantitative results show that the proposed method achieves superior performance compared with other unsupervised harmonization approaches.