Is attention all you need in medical image analysis? A review

G Papanastasiou, N Dikaios, J Huang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging

X Xu, L Lin, S Sun, S Wu - Reviews in the Neurosciences, 2023 - degruyter.com
Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible
cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early …

Applications of deep learning in Alzheimer's disease: A systematic literature review of current trends, methodologies, challenges, innovations, and future directions

S Toumaj, A Heidari, R Shahhosseini… - Artificial Intelligence …, 2024 - Springer
Alzheimer's Disease (AD) constitutes a significant global health issue. In the next 40 years, it
is expected to affect 106 million people. Although more and more people are getting AD …

Investigating deep learning for early detection and decision-making in alzheimer's disease: a comprehensive review

G Hcini, I Jdey, H Dhahri - Neural Processing Letters, 2024 - Springer
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions of people
worldwide, making early detection essential for effective intervention. This review paper …

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases

T Wang, X Chen, J Zhang, Q Feng, M Huang - Medical Image Analysis, 2023 - Elsevier
Imaging genetics is a crucial tool that is applied to explore potentially disease-related
biomarkers, particularly for neurodegenerative diseases (NDs). With the development of …

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks

X Zhu, S Sun, L Lin, Y Wu, X Ma - Reviews in the Neurosciences, 2025 - degruyter.com
In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a
formidable neural network architecture, gaining significant traction in neuroimaging-based …

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies

AS Alatrany, W Khan, A Hussain, D Al-Jumeily… - Plos one, 2023 - journals.plos.org
The increasing incidence of Alzheimer's disease (AD) has been leading towards a
significant growth in socioeconomic challenges. A reliable prediction of AD might be useful …

Modality-aware discriminative fusion network for integrated analysis of brain imaging genomics

X Sheng, H Cai, Y Nie, S He… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD),
characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The …

Deep learning-based feature extraction with MRI data in neuroimaging genetics for Alzheimer's disease

D Chakraborty, Z Zhuang, H Xue, MB Fiecas, X Shen… - Genes, 2023 - mdpi.com
The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been
among the most important and challenging problems over the last few decades. To better …

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph …

W Peng, Y Ma, C Li, W Dai, X Fu, L Liu, L Liu, J Liu - Neural Networks, 2025 - Elsevier
Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and
genes play a vital role in understanding the AD and develo** effective prevention and …