ITFuse: An interactive transformer for infrared and visible image fusion

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

Опубликована: Июль 31, 2024

Язык: Английский

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MMIF-INet: Multimodal medical image fusion by invertible network

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

Опубликована: Сен. 4, 2024

Язык: Английский

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MARCFusion: adaptive residual cross-domain fusion network for medical image fusion

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

Опубликована: Фев. 7, 2025

Язык: Английский

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Breaking data barriers in medical diagnosis with MSDGD framework based on Gaussian Diffusion Generation

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

Опубликована: Март 23, 2025

Язык: Английский

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A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

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

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Diffusion-driven multi-modality medical image fusion

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

Опубликована: Фев. 11, 2025

Язык: Английский

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SDRD-Net: A Symmetric Dual-branch Residual Dense Network for OCT and US Image Fusion

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

Опубликована: Фев. 1, 2025

Язык: Английский

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SSEFusion: Salient semantic enhancement for multimodal medical image fusion with Mamba and dynamic spiking neural networks

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

Опубликована: Фев. 1, 2025

Язык: Английский

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Expert guidance and partially-labeled data collaboration for multi-organ segmentation

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

Опубликована: Март 1, 2025

Язык: Английский

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

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

Опубликована: Март 1, 2025

Язык: Английский

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