Transformer’s Role in Brain MRI: A Scoping Review

Hayat, Mansoor; Aramvith, Supavadee

doi:10.1109/access.2024.3434714

IEEE Access

2024

DOI: 10.1109/access.2024.3434714

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Transformer’s Role in Brain MRI: A Scoping Review

Mansoor Hayat,

Supavadee Aramvith

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2024

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Cited by 3 publications

References 199 publications

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Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution

Hayat

2024

Franklin Open

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Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution

Hayat

2024

Franklin Open

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Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

Chauhan,

Lunagaria,

Verma

et al. 2024

IEEE Access

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MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

Li,

Yang,

Bian

et al. 2024

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This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

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Transformer’s Role in Brain MRI: A Scoping Review

Cited by 3 publications

References 199 publications

Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution

Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution

Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

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