# Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

arXiv: https://arxiv.org/abs/2201.06505 · HTML: https://mlsanity.com/paper/2201.06505/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 172 references that are in the map.
- First posted on arXiv 17 Jan 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study](https://mlsanity.com/paper/1911.00515/) (2019)
- [Scanner Invariant Representations for Diffusion MRI Harmonization](https://mlsanity.com/paper/1904.05375/) (2019)
- [Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization](https://mlsanity.com/paper/2102.06315/) (2021)
- [Learning by Association - A versatile semi-supervised training method for neural networks](https://mlsanity.com/paper/1706.00909/) (2017)
- [Removal of Batch Effects using Distribution-Matching Residual Networks](https://mlsanity.com/paper/1610.04181/) (2016)
- [Spherical Harmonic Residual Network for Diffusion Signal Harmonization](https://mlsanity.com/paper/1808.01595/) (2018)
- [Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising](https://mlsanity.com/paper/1606.07239/) (2016)
- [Harmonization of diffusion MRI datasets with adaptive dictionary learning](https://mlsanity.com/paper/1910.00272/) (2019)

## Built on by (most cited first, 19 of 19)

- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [High-Frequency Space Diffusion Models for Accelerated MRI](https://mlsanity.com/paper/2208.05481/) (2022)
- [Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review](https://mlsanity.com/paper/2203.15588/) (2022)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [Federated Multi-organ Segmentation with Inconsistent Labels](https://mlsanity.com/paper/2206.07156/) (2022)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (2022)
- [Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge](https://mlsanity.com/paper/2312.13752/) (2023)
- [Beyond the Hype: A dispassionate look at vision-language models in medical scenario](https://mlsanity.com/paper/2408.08704/) (2024)
- [Brain tumour segmentation with incomplete imaging data](https://mlsanity.com/paper/2206.06120/) (2022)
- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework](https://mlsanity.com/paper/2302.11795/) (2023)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Fast MRI Reconstruction via Edge Attention](https://mlsanity.com/paper/2304.11400/) (2023)
- [Revisiting Medical Image Retrieval via Knowledge Consolidation](https://mlsanity.com/paper/2503.09370/) (2025)
- [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025)
- [Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare](https://mlsanity.com/paper/2505.20020/) (2025)
- [Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers](https://mlsanity.com/paper/2204.01706/) (2022)
- [Texture-Aware StarGAN for CT data harmonisation](https://mlsanity.com/paper/2503.15058/) (2025)

## Abstract (arXiv)

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.