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 …

Review of Artificial Intelligence in Lung Nodule Risk Assessment

Y Wei, Q Zhou, J Wu, X Xu, Y Gao… - IEEE Reviews in …, 2025 - ieeexplore.ieee.org
Lung cancer is the leading cause of cancerrelated mortality worldwide. In addition to
localizing and segmenting lung nodules, a non-invasive risk assessment system can also …

[HTML][HTML] A multi-task learning model for clinically interpretable sesamoiditis grading

L Guo, AM Tahir, M Hore, A Collins, A Rideout… - Computers in Biology …, 2024 - Elsevier
Sesamoiditis is a common equine disease with varying severity, leading to increased injury
risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for …

Vision transformer promotes cancer diagnosis: A comprehensive review

X Jiang, S Wang, Y Zhang - Expert Systems with Applications, 2024 - Elsevier
Background The approaches based on vision transformers (ViTs) are advancing the field of
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …

Faster 3D cardiac CT segmentation with Vision Transformers

L Jollans, M Bustamante, L Henriksson… - arXiv preprint arXiv …, 2023 - arxiv.org
Accurate segmentation of the heart is essential for personalized blood flow simulations and
surgical intervention planning. A recent advancement in image recognition is the Vision …

A Siamese U-Transformer for change detection on MRI brain for multiple sclerosis, a model development and external validation study.

BS Kelly, P Mathur, RP Killeen, A Lawlor - medRxiv, 2024 - medrxiv.org
Background Multiple Sclerosis (MS), is a chronic idiopathic demyelinating disorder of the
CNS. Imaging plays a central role in diagnosis and monitoring. Monitoring for progression …

Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study

K Kim, K Cho, Y Eo, J Kim, J Yun, Y Ahn, JB Seo… - Journal of Imaging …, 2024 - Springer
We aimed to evaluate the ability of deep learning (DL) models to identify patients from a
paired chest radiograph (CXR) and compare their performance with that of human experts …