CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach

Information Fusion, Journal Year: 2023, Volume and Issue: 103, P. 102147 - 102147

Published: Nov. 15, 2023

Language: Английский

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Medical image super-resolution for smart healthcare applications: A comprehensive survey

Information Fusion, Journal Year: 2023, Volume and Issue: 103, P. 102075 - 102075

Published: Oct. 18, 2023

The digital transformation in healthcare, propelled by the integration of deep learning models and Internet Things (IoT), is creating unprecedented opportunities for improving patient care. However, utilization low-resolution images, often generated IoT devices, introduces biases models, thereby affecting overall clinical decision-making process. While super-resolution techniques have been extensively employed to transform images into high-resolution counterparts, challenge achieving highly accurate image restoration remains unresolved. This especially critical medical imaging domain, where even minor inaccuracies can lead significant model training and, consequently, impact outcomes. Although existing surveys explored various methods their applications across different fields, a comprehensive review emphasizing accuracy its subsequent influence on notably lacking. survey seeks bridge this gap offering systematic current state-of-the-art highlighting limitations surveys, underscoring open questions that merit further research. Specifically, we delve intricacies restoration, identify research gaps unmet challenges optimal emphasize crucial role developing more precise resilient enhance quality performance healthcare applications. Ultimately, fosters deeper comprehension prevailing unresolved field, thus setting stage future efforts focused refining subsequently, boosting efficacy healthcare.

Language: Английский

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Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 160, P. 106998 - 106998

Published: May 6, 2023

Language: Английский

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Multi-source information fusion: Progress and future

Chinese Journal of Aeronautics, Journal Year: 2023, Volume and Issue: 37(7), P. 24 - 58

Published: Dec. 12, 2023

Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field based on modern information technology, has gained significant research value and extensive application prospects in various domains, attracting high attention interest from scholars, engineering experts, practitioners worldwide. Despite achieving fruitful results both theoretical applied aspects over the past five decades, there remains lack of systematic review articles that provide an overview recent development MSIF. In light this, this paper aims to assist researchers individuals interested gaining quick understanding relevant techniques trends MSIF, which conducts statistical analysis academic reports related achievements MSIF two provides brief theories, methodologies, well key issues challenges currently faced. Finally, outlook future directions are presented.

Language: Английский

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A review of image fusion: Methods, applications and performance metrics

Digital Signal Processing, Journal Year: 2023, Volume and Issue: 137, P. 104020 - 104020

Published: March 24, 2023

Language: Английский

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Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 35728 - 35753

Published: Jan. 1, 2024

Generative Adversarial Networks are a class of artificial intelligence algorithms that consist generator and discriminator trained simultaneously through adversarial training. GANs have found crucial applications in various fields, including medical imaging. In healthcare, contribute by generating synthetic images, enhancing data quality, aiding image segmentation, disease detection, synthesis. Their importance lies their ability to generate realistic facilitating improved diagnostics, research, training for professionals. Understanding its applications, algorithms, current advancements, challenges is imperative further advancement the imaging domain. However, no study explores recent state-of-the-art development To overcome this research gap, extensive study, we began exploring vast array imaging, scrutinizing them within research. We then dive into prevalent datasets pre-processing techniques enhance comprehension. Subsequently, an in-depth discussion GAN elucidating respective strengths limitations, provided. After that, meticulously analyzed results experimental details some cutting-edge obtain more comprehensive understanding Lastly, discussed diverse encountered future directions mitigate these concerns. This systematic review offers complete overview encompassing application domains, models, analysis, challenges, directions, serving as valuable resource multidisciplinary studies.

Language: Английский

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A review of cancer data fusion methods based on deep learning

Information Fusion, Journal Year: 2024, Volume and Issue: 108, P. 102361 - 102361

Published: March 20, 2024

Language: Английский

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Semi-supervised fault diagnosis of gearbox based on feature pre-extraction mechanism and improved generative adversarial networks under limited labeled samples and noise environment

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 58, P. 102211 - 102211

Published: Oct. 1, 2023

Language: Английский

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FATFusion: A functional–anatomical transformer for medical image fusion

Information Processing & Management, Journal Year: 2024, Volume and Issue: 61(4), P. 103687 - 103687

Published: March 11, 2024

Language: Английский

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Self-supervised spatial–temporal transformer fusion based federated framework for 4D cardiovascular image segmentation

Information Fusion, Journal Year: 2024, Volume and Issue: 106, P. 102256 - 102256

Published: Jan. 30, 2024

Availability of high-quality large annotated data is indeed a significant challenge in healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to diverse medical image datasets. To reduce the requirement for training data, self-supervised pre-training strategies on nonannotated have been extensively used, whereas collaborative algorithm without need exchange underlying data. this paper, we introduce novel federated learning-based spatial–temporal transformer's fusion (SSFL) cardiovascular segmentation. The integration swin transformer used extract features from 3D SAX multiple phases (full cycle cardiac heart). An efficient contrastive framework consisting network with 25 encoders model temporal features. spatial are fused forwarded decoder heart segmentation using cine MRI images. further improve segmentation, use an attention-based unpaired GAN map or transfer style ACDC M&Ms synthetically generated volumes proposed approach. Experiments three different tasks, such as right ventricle, left myocardium, showed improvement compared state-of-the-art framework.

Language: Английский

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