{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:44:19Z","timestamp":1765896259880,"version":"3.48.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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% in semantic segmentation (mIoU). The model was benchmarked not only against classical ML methods (e.g., DWT\u202f+\u202fSVM, LBP\u202f+\u202fSVM) but also modern deep learning architectures and attention-based fusion models, confirming the superiority and novelty of the proposed SICF framework. A 5-fold cross-validation on MSRS demonstrated consistent performance (78.21\u202f\u00b1\u202f1.02 mAP, 71.45\u202f\u00b1\u202f1.18 mIoU). Model complexity analysis confirmed efficiency in terms of parameters, FLOPs, and inference time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>TSJNet effectively combines task-aware supervision and modality interaction to produce high-quality fused outputs. Its performance, robustness, and efficiency make it a promising solution for real-world multi-modal imaging applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1606570","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:56:14Z","timestamp":1765886174000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An intelligent MRI data fusion framework for optimized diagnosis of spinal 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