A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

Information Fusion, Год журнала: 2023, Номер 96, С. 156 - 191

Опубликована: Март 15, 2023

Язык: Английский

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Emotion recognition in EEG signals using deep learning methods: A review

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107450 - 107450

Опубликована: Сен. 9, 2023

Язык: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

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PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images

Journal of Digital Imaging, Год журнала: 2023, Номер 36(3), С. 973 - 987

Опубликована: Фев. 16, 2023

Язык: Английский

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Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Information Fusion, Год журнала: 2024, Номер 110, С. 102472 - 102472

Опубликована: Май 16, 2024

Язык: Английский

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A review of deep learning-based information fusion techniques for multimodal medical image classification

Computers in Biology and Medicine, Год журнала: 2024, Номер 177, С. 108635 - 108635

Опубликована: Май 22, 2024

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various modalities to provide more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged powerful tools for improving image classification. This review offers thorough analysis developments classification tasks. We explore complementary relationships among prevalent outline three main schemes networks: input fusion, intermediate (encompassing single-level hierarchical attention-based fusion), output fusion. By evaluating performance these techniques, we insight into suitability different network architectures scenarios application domains. Furthermore, delve challenges related architecture selection, handling incomplete data management, potential limitations Finally, spotlight promising future Transformer-based give recommendations research this rapidly evolving field.

Язык: Английский

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Deep Learning Models for Diagnosis of Schizophrenia Using EEG Signals: Emerging Trends, Challenges, and Prospects

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(4), С. 2345 - 2384

Опубликована: Янв. 6, 2024

Язык: Английский

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DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction

Magnetic Resonance Imaging, Год журнала: 2024, Номер 107, С. 69 - 79

Опубликована: Янв. 17, 2024

Язык: Английский

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Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Diagnostics, Год журнала: 2025, Номер 15(4), С. 456 - 456

Опубликована: Фев. 13, 2025

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights advanced algorithmic methods in pursuit of an enhanced understanding and applications recognition. Methods: study was conducted PRISMA guidelines, involving a rigorous selection process that resulted the inclusion 64 empirical studies explore modalities such as fMRI, EEG, MEG, discussing their capabilities limitations It further evaluates architectures, including neural networks, CNNs, GANs, terms roles classifying emotions from various domains: human-computer interaction, mental health, marketing, more. Ethical practical challenges implementing these systems are also analyzed. Results: identifies fMRI powerful but resource-intensive modality, while EEG MEG more accessible high temporal resolution limited by spatial accuracy. Deep models, especially CNNs have performed well emotions, though they do not always require large diverse datasets. Combining data behavioral features improves classification performance. However, ethical challenges, privacy bias, remain significant concerns. Conclusions: has emphasized efficiencies detection, technical were highlighted. Future research should integrate advances, establish innovative enhance system reliability applicability.

Язык: Английский

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Causal knowledge fusion for 3D cross-modality cardiac image segmentation

Information Fusion, Год журнала: 2023, Номер 99, С. 101864 - 101864

Опубликована: Июнь 2, 2023

Язык: Английский

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