A systematic review of vision transformers and convolutional neural networks for Alzheimer's disease classification using 3D MRI images

MA Bravo-Ortiz, SA Holguin-Garcia… - Neural Computing and …, 2024 - Springer
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that mainly affects
memory and other cognitive functions, such as thinking, reasoning, and the ability to carry …

[HTML][HTML] Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review

TD Le, NC Shitiri, SH Jung, SY Kwon, C Lee - Sensors, 2024 - mdpi.com
Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various
diseases; however, challenges persist regarding image quality and accessibility during NMI …

Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine

X Xia, Y Guo, Y Wang, Y Yang, Y Shi, H Men - Expert Systems with …, 2024 - Elsevier
Odor sensory evaluation is broadly applied in food, clothing, cosmetics, and other fields.
Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction …

AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans

G Lozupone, A Bria, F Fontanella, FJA Meijer… - arXiv preprint arXiv …, 2024 - arxiv.org
This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI
designed to enhance the explainability of model decisions. Our approach adopts a soft …

Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach

J Sun, JDJ Han, W Chen - Scientific Reports, 2024 - nature.com
Alzheimer's disease (AD) is a fatal neurodegenerative disorder, with the Mini-Mental State
Examination (MMSE) and Clinical Dementia Rating (CDR) serving significant roles in …

A 3D multi-scale CycleGAN framework for generating synthetic PETs from MRIs for Alzheimer's disease diagnosis

M Khojaste-Sarakhsi, SS Haghighi… - Image and Vision …, 2024 - Elsevier
This paper proposes a novel framework for generating synthesized PET images from MRIs
to fill in missing PETs and help with Alzheimer's disease (AD) diagnosis. This framework …

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment

C Wang, Y Lei, T Chen, J Zhang, Y Li… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease
(AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype …

Deep Learning Techniques for Automated Dementia Diagnosis Using Neuroimaging Modalities: A Systematic Review (2012-2023)

D Ozkan, O Katar, M Ak, MA Al-Antari, NY Ak… - IEEE …, 2024 - ieeexplore.ieee.org
Dementia is a condition that often comes with aging and affects how people think,
remember, and behave. Diagnosing dementia early is important because it can greatly …

A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer's Disease Diagnosis

Z Ou, C Jiang, Y Liu, Y Zhang, Z Cui, D Shen - … Conference on Medical …, 2024 - Springer
Alzheimer's disease (AD) is an irreversible neurodegenerative disease, where early
diagnosis is crucial for improving prognosis and delaying the progression of the disease …

FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on

C Wang, T Chen, Z Chen, Z Huang, T Jiang… - arXiv preprint arXiv …, 2024 - arxiv.org
Despite their impressive generative performance, latent diffusion model-based virtual try-on
(VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and …