# A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

arXiv: https://arxiv.org/abs/2505.14715 · HTML: https://mlsanity.com/paper/2505.14715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 351 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 13 of 13)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024)
- [Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion](https://mlsanity.com/paper/1910.04066/) (2019)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [Medical Image Fusion: A survey of the state of the art](https://mlsanity.com/paper/1401.0166/) (2013)
- [Computational Pathology: Challenges and Promises for Tissue Analysis](https://mlsanity.com/paper/1601.00027/) (2015)
- [ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning](https://mlsanity.com/paper/2312.07943/) (2023)
- [Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes](https://mlsanity.com/paper/1711.00049/) (2017)
- [Multimodal Fusion Learning with Dual Attention for Medical Imaging](https://mlsanity.com/paper/2412.01248/) (2024)
- [Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems](https://mlsanity.com/paper/2104.04000/) (2021)
- [Multimodal and multicontrast image fusion via deep generative models](https://mlsanity.com/paper/2303.15963/) (2023)
- [Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection](https://mlsanity.com/paper/2101.07342/) (2021)

## Built on by (most cited first, 2 of 2)

- [J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization](https://mlsanity.com/paper/2510.09953/) (2025)
- [Domain Generalization with Quantum Enhancement for Medical Image Classification: A Lightweight Approach for Cross-Center Deployment](https://mlsanity.com/paper/2601.17862/) (2026)

## Abstract (arXiv)

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance.