LightFusionNet: Lightweight Dual-Stream Network with Predictive Context Attention for Efficient Medical Image Fusion

Abhinav Sagar
Proceedings of The Second AAAI Bridge Program on AI for Medicine and Healthcare, PMLR 317:210-218, 2026.

Abstract

Multimodal image fusion aims to integrate complementary information from multiple imaging modalities into a single, informative representation, which is crucial for applications in medical imaging and microscopy. Existing methods often face trade-offs between structural fidelity, edge preservation, and computational efficiency. In this work, we propose Light-FusionNet, a lightweight dual-stream network designed to efficiently fuse multimodal images while retaining key structural, textural, and intensity features. The network leverages depthwise separable convolutions to reduce model complexity and incorporates a Predictive Context Attention (PCA) mechanism to selectively emphasize informative regions in the feature maps. Extensive experiments on benchmark medical imaging datasets, including PET-MRI, SPECT-MRI, and CT-MRI, demonstrate that our approach achieves comparable qualitative and quantitative performance compared to state-of-the-art fusion methods, while maintaining low computational cost. The proposed method provides an effective and efficient solution for multimodal image fusion, suitable for both clinical and research applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v317-sagar26a, title = {LightFusionNet: Lightweight Dual-Stream Network with Predictive Context Attention for Efficient Medical Image Fusion}, author = {Sagar, Abhinav}, booktitle = {Proceedings of The Second AAAI Bridge Program on AI for Medicine and Healthcare}, pages = {210--218}, year = {2026}, editor = {Wu, Junde and Pan, Jiazhen and Zhu, Jiayuan and Luo, Luyang and Li, Yitong and Xu, Min and Jin, Yueming and Rueckert, Daniel}, volume = {317}, series = {Proceedings of Machine Learning Research}, month = {20--21 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v317/main/assets/sagar26a/sagar26a.pdf}, url = {https://proceedings.mlr.press/v317/sagar26a.html}, abstract = {Multimodal image fusion aims to integrate complementary information from multiple imaging modalities into a single, informative representation, which is crucial for applications in medical imaging and microscopy. Existing methods often face trade-offs between structural fidelity, edge preservation, and computational efficiency. In this work, we propose Light-FusionNet, a lightweight dual-stream network designed to efficiently fuse multimodal images while retaining key structural, textural, and intensity features. The network leverages depthwise separable convolutions to reduce model complexity and incorporates a Predictive Context Attention (PCA) mechanism to selectively emphasize informative regions in the feature maps. Extensive experiments on benchmark medical imaging datasets, including PET-MRI, SPECT-MRI, and CT-MRI, demonstrate that our approach achieves comparable qualitative and quantitative performance compared to state-of-the-art fusion methods, while maintaining low computational cost. The proposed method provides an effective and efficient solution for multimodal image fusion, suitable for both clinical and research applications.} }
Endnote
%0 Conference Paper %T LightFusionNet: Lightweight Dual-Stream Network with Predictive Context Attention for Efficient Medical Image Fusion %A Abhinav Sagar %B Proceedings of The Second AAAI Bridge Program on AI for Medicine and Healthcare %C Proceedings of Machine Learning Research %D 2026 %E Junde Wu %E Jiazhen Pan %E Jiayuan Zhu %E Luyang Luo %E Yitong Li %E Min Xu %E Yueming Jin %E Daniel Rueckert %F pmlr-v317-sagar26a %I PMLR %P 210--218 %U https://proceedings.mlr.press/v317/sagar26a.html %V 317 %X Multimodal image fusion aims to integrate complementary information from multiple imaging modalities into a single, informative representation, which is crucial for applications in medical imaging and microscopy. Existing methods often face trade-offs between structural fidelity, edge preservation, and computational efficiency. In this work, we propose Light-FusionNet, a lightweight dual-stream network designed to efficiently fuse multimodal images while retaining key structural, textural, and intensity features. The network leverages depthwise separable convolutions to reduce model complexity and incorporates a Predictive Context Attention (PCA) mechanism to selectively emphasize informative regions in the feature maps. Extensive experiments on benchmark medical imaging datasets, including PET-MRI, SPECT-MRI, and CT-MRI, demonstrate that our approach achieves comparable qualitative and quantitative performance compared to state-of-the-art fusion methods, while maintaining low computational cost. The proposed method provides an effective and efficient solution for multimodal image fusion, suitable for both clinical and research applications.
APA
Sagar, A.. (2026). LightFusionNet: Lightweight Dual-Stream Network with Predictive Context Attention for Efficient Medical Image Fusion. Proceedings of The Second AAAI Bridge Program on AI for Medicine and Healthcare, in Proceedings of Machine Learning Research 317:210-218 Available from https://proceedings.mlr.press/v317/sagar26a.html.

Related Material