A transformer-based unified multimodal framework for Alzheimer's disease assessment
In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often
employed separate methodologies to handle the diverse modalities of input data …
employed separate methodologies to handle the diverse modalities of input data …
Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks
İM Baytaş - IEEE Journal of Biomedical and Health Informatics, 2024 - ieeexplore.ieee.org
Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might
slow down the disease's progression. This problem is commonly posed as a classification …
slow down the disease's progression. This problem is commonly posed as a classification …
Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability
X Zhou, S Kedia, R Meng, M Gerstein - PloS one, 2024 - journals.plos.org
The early detection of Alzheimer's Disease (AD) is thought to be important for effective
intervention and management. Here, we explore deep learning methods for the early …
intervention and management. Here, we explore deep learning methods for the early …
for differential diagnosis of dementia
VB Kolachalama - Nature Medicine, 2024 - nature.com
Using routinely collected multimodal clinical data, we developed an artificial intelligence (AI)
model to identify dementia and determine factors causing it, including mixed dementias and …
model to identify dementia and determine factors causing it, including mixed dementias and …
Adversarial learning for MRI reconstruction and classification of cognitively impaired individuals
Game theory-inspired deep learning using a generative adversarial network provides an
environment to competitively interact and accomplish a goal. In the context of medical …
environment to competitively interact and accomplish a goal. In the context of medical …