---
title: 'FlexiD-Fuse: Diffusion Fusion for Medical Imaging'
url: https://www.emergentmind.com/topics/flexid-fuse
type: topic
---

# FlexiD-Fuse: Diffusion Fusion for Medical Imaging

Searching arXiv for the target paper and a small set of directly relevant diffusion/fusion references.
FlexiD-Fuse is a diffusion-based image fusion network for multi-modal medical image fusion that is designed to accommodate flexible quantities of input modalities and can end-to-end process two-modal and tri-modal fusion under the same weight [2509.09456]. The method addresses a stated limitation of existing fusion systems, namely that they are typically specialized to a fixed number of inputs and therefore cannot directly process varying input quantities in clinical settings. Its central reformulation converts diffusion fusion from a fixed-condition input problem into a maximum likelihood estimation problem based on the diffusion process and hierarchical Bayesian modeling, with an Expectation-Maximization (EM) procedure embedded into diffusion sampling so that fused images can be generated independently of the number of input images [2509.09456].

## 1. Problem setting and design objective

The immediate target of FlexiD-Fuse is medical image fusion across modalities such as MRI, CT, PET, and SPECT, where different source images provide complementary physiological and anatomical information [2509.09456]. In the formulation described for prior fixed-input systems, traditional fusion networks are represented as input-number-specific functions such as $\mathrm{F}_{mf_2}$ and $\mathrm{F}_{mf_3}$, each trained for a specific modality count and therefore requiring separate architectures and weights.

FlexiD-Fuse replaces that regime with a generalized fusion function,
$$
I_f = \mathrm{F}_{gf}(I_{mod_1}, \ldots, I_{mod_n}; \theta_g),
$$
where $n$ is flexible and $\theta_g$ is shared [2509.09456]. In the reported medical setting, inference is performed for $n=2$ or $n=3$, but the framework is presented as supporting arbitrary input quantities through a single unified architecture and weight setting. The paper further states that this is achieved without modality-specific fusion rules and without ground truth fusion images.

A common misconception in this problem area is that “flexible-input” fusion can be reduced to training multiple specialized models and selecting one at inference time. FlexiD-Fuse is defined against precisely that assumption: its claim is not merely multi-configuration compatibility, but end-to-end fusion under shared weights with dynamic adjustment of modality contributions during inference [2509.09456].

## 2. Architectural composition

The architecture combines

Source: https://www.emergentmind.com/topics/flexid-fuse