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

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 103, С. 107357 - 107357

Опубликована: Дек. 28, 2024

Язык: Английский

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

и другие.

Pattern Recognition, Год журнала: 2024, Номер 156, С. 110822 - 110822

Опубликована: Июль 31, 2024

Язык: Английский

Процитировано

10

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

и другие.

Information Fusion, Год журнала: 2024, Номер 114, С. 102666 - 102666

Опубликована: Сен. 4, 2024

Язык: Английский

Процитировано

9

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

и другие.

Information Processing & Management, Год журнала: 2025, Номер 62(4), С. 104130 - 104130

Опубликована: Март 23, 2025

Язык: Английский

Процитировано

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

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 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.

Язык: Английский

Процитировано

4

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

Yu Shao

и другие.

Multimedia Systems, Год журнала: 2025, Номер 31(2)

Опубликована: Фев. 7, 2025

Язык: Английский

Процитировано

0

Diffusion-driven multi-modality medical image fusion DOI

Jiantao Qu,

Dongjin Huang,

Yongsheng Shi

и другие.

Medical & Biological Engineering & Computing, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Язык: Английский

Процитировано

0

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

и другие.

Ultrasound in Medicine & Biology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

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

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 103031 - 103031

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

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

и другие.

Neural Networks, Год журнала: 2025, Номер unknown, С. 107396 - 107396

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

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

Zifang Zhou,

Hao Li

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113052 - 113052

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0