@article{MIAO2025, 
author = {XuFan MIAO and YingYue QI and DongMei YANG and ChengXia WANG and LiGuo YANG and ZiHan WEI and Ping YI and JianLin WANG},
title = {A multimodal medical image fusion method based on a cross-modal channel-aware (CMCA) module},
year = {2025},
journal = {Journal of Beijing University of Chemical Technology (Natural Science Edition)},
volume = {52},
number = {6},
pages = {109-118},
keywords = {medical image fusion, deep learning, channel attention},
url = {https://www.sciopen.com/article/10.13543/j.bhxbzr.2025.06.013},
doi = {10.13543/j.bhxbzr.2025.06.013},
abstract = {To address the problem of noise degrading fusion performance in medical images, we propose a multimodal medical image fusion method based on cross-modal channel-aware (CMCA) module. A dual-branch encoder enhanced with a squeeze-and-excitation module is introduced to apply channel-wise weighting during feature extraction and obtain modality-specific features. A cross-modal channel-aware fusion module is constructed to integrate the extracted information and achieve complementary feature fusion. A composite loss function combining image entropy and median-based weighting is adopted to preserve detail while suppressing noise during training, thereby enabling effective multimodal medical image fusion. Experimental results on an MRI-CT dataset show that the proposed method achieves an average gradient of 8.630, a standard deviation of 82.301 with a spatial frequency of 35.728, and a structural similarity index of 1.173, which makes it a valuable tool for assisting clinicians in lesion analysis.}
}