AFPNet: An adaptive frequency-domain optimized progressive medical image fusion network DOI
Dangguo Shao, Hongjuan Yang, Lei Ma

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 103, P. 107357 - 107357

Published: Dec. 28, 2024

Language: Английский

ITFuse: An interactive transformer for infrared and visible image fusion DOI
Wei Tang, Fazhi He, Yü Liu

et al.

Pattern Recognition, Journal Year: 2024, Volume and Issue: 156, P. 110822 - 110822

Published: July 31, 2024

Language: Английский

Citations

12
MMIF-INet: Multimodal medical image fusion by invertible network DOI
Dan He, Weisheng Li, Guofen Wang

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: 114, P. 102666 - 102666

Published: Sept. 4, 2024

Language: Английский

Citations

10
MARCFusion: adaptive residual cross-domain fusion network for medical image fusion DOI
Haozhe Tang, Lei Yu,

Yu Shao

et al.

Multimedia Systems, Journal Year: 2025, Volume and Issue: 31(2)

Published: Feb. 7, 2025

Language: Английский

Citations

1
Breaking data barriers in medical diagnosis with MSDGD framework based on Gaussian Diffusion Generation DOI
Fengwei Jia, Fengyuan Jia, Huale Li

et al.

Information Processing & Management, Journal Year: 2025, Volume and Issue: 62(4), P. 104130 - 104130

Published: March 23, 2025

Language: Английский

Citations

1
A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion DOI Creative Commons
Cong Lin, Yinjie Chen,

Siling Feng

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 30, 2024

Abstract Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer poor quality loss of crucial details due to inadequate handling semantic limited feature extraction capabilities. This paper introduces a novel medical technique leveraging unsupervised segmentation enhance the understanding process. The proposed method, named DUSMIF, employs multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms refine processes. An innovative approach utilizes extract introduced, which then integrated into not only enhances relevance fused images but also improves overall quality. proposes sophisticated network structure extracts fuses features at scales across branches. designed capture comprehensive range contextual information, significantly improving outcomes. Multiple are incorporated selectively emphasize important integrate them effectively different scales. ensures maintain high detail fidelity. A joint function combining content loss, structural similarity formulated. guides preserving brightness texture closely resembles source both structure. method demonstrates superior performance over existing techniques objective assessments subjective evaluations, confirming its effectiveness enhancing diagnostic utility images.

Language: Английский

Citations

5
Diffusion-driven multi-modality medical image fusion DOI

Jiantao Qu,

Dongjin Huang,

Yongsheng Shi

et al.

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

Citations

0
SDRD-Net: A Symmetric Dual-branch Residual Dense Network for OCT and US Image Fusion DOI
Xiao Zhang, Bin He, Zhiyi Chen

et al.

Ultrasound in Medicine & Biology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
SSEFusion: Salient semantic enhancement for multimodal medical image fusion with Mamba and dynamic spiking neural networks DOI
Shiqiang Liu, Weisheng Li, Dan He

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103031 - 103031

Published: Feb. 1, 2025

Language: Английский

Citations

0
Expert guidance and partially-labeled data collaboration for multi-organ segmentation DOI
Li Li, Jianyi Liu, Hanguang Xiao

et al.

Neural Networks, Journal Year: 2025, Volume and Issue: unknown, P. 107396 - 107396

Published: March 1, 2025

Language: Английский

Citations

0
A morphological difference and statistically sparse Transformer-based deep neural network for medical image segmentation DOI
Dongxu Cheng,

Zifang Zhou,

Hao Li

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113052 - 113052

Published: March 1, 2025

Language: Английский

Citations

0