An intelligent MRI data fusion framework for optimized diagnosis of spinal tumors.

ABSTRACT:

Background

Multi-modal image fusion is essential for combining complementary information from heterogeneous sensors to support downstream vision tasks. However, existing methods often focus on a single objective, limiting their effectiveness in complex real-world scenarios.

Methods

We propose TSJNet, a novel Target and Semantic Joint-driven Network for multi-modality image fusion. The architecture integrates a fusion module with detection and segmentation subnetworks. A Local Significant Feature Extraction (LSFE) module with dual-branch design enhances fine-grained cross-modal feature interaction.

Results

TSJNet was evaluated on four public datasets (MSRS, M3FD, RoadScene, and LLVIP), achieving an average improvement of +2.84% in object detection (mAP@0.5) and +7.47%

SUBMITTER: Shi Z 

PROVIDER: S-EPMC12745385 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature