# A Semantic-based Medical Image Fusion Approach

arXiv: https://arxiv.org/abs/1906.00225 · HTML: https://mlsanity.com/paper/1906.00225/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis](https://mlsanity.com/paper/2505.12251/) (2025)
- [Semantic Image Fusion](https://mlsanity.com/paper/2110.06697/) (2021)
- [Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder](https://mlsanity.com/paper/2310.11910/) (2023)

## Abstract (arXiv)

It is necessary for clinicians to comprehensively analyze patient information from different sources. Medical image fusion is a promising approach to providing overall information from medical images of different modalities. However, existing medical image fusion approaches ignore the semantics of images, making the fused image difficult to understand. In this work, we propose a new evaluation index to measure the semantic loss of fused image, and put forward a Fusion W-Net (FW-Net) for multimodal medical image fusion. The experimental results are promising: the fused image generated by our approach greatly reduces the semantic information loss, and has better visual effects in contrast to five state-of-art approaches. Our approach and tool have great potential to be applied in the clinical setting.