[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study
Optimization algorithms are used to improve model accuracy. The optimization process
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
CCC-SSA-UNet: U-Shaped Pansharpening Network with Channel Cross-Concatenation and Spatial–Spectral Attention Mechanism for Hyperspectral Image Super …
A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the
target's material properties well. However, the limited spatial resolution poses a constraint …
target's material properties well. However, the limited spatial resolution poses a constraint …
Benchmarking geometric deep learning for cortical segmentation and neurodevelopmental phenotype prediction
The emerging field of geometric deep learning extends the application of convolutional
neural networks to irregular domains such as graphs, meshes and surfaces. Several recent …
neural networks to irregular domains such as graphs, meshes and surfaces. Several recent …
Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning
A Valoor, GR Gangadharan - Journal of Neuroscience Methods, 2025 - Elsevier
Abstract Background The field of Alzheimer's disease (AD) diagnosis is undergoing
significant transformation due to the application of deep learning (DL) models. While DL …
significant transformation due to the application of deep learning (DL) models. While DL …
Applications of interpretable deep learning in neuroimaging: a comprehensive review
L Munroe, M da Silva, F Heidari, I Grigorescu… - Imaging …, 2024 - direct.mit.edu
Clinical adoption of deep learning models has been hindered, in part, because the “black-
box” nature of neural networks leads to concerns regarding their trustworthiness and …
box” nature of neural networks leads to concerns regarding their trustworthiness and …
Voxel-level importance maps for interpretable brain age estimation
Brain aging, and more specifically the difference between the chronological and the
biological age of a person, may be a promising biomarker for identifying neurodegenerative …
biological age of a person, may be a promising biomarker for identifying neurodegenerative …
Towards better interpretable and generalizable AD detection using collective artificial intelligence
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and
prognosis of this disease are essential to design an appropriate treatment plan, increasing …
prognosis of this disease are essential to design an appropriate treatment plan, increasing …
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
HS Moon, A Mahzarnia, J Stout, RJ Anderson… - Imaging …, 2024 - direct.mit.edu
Abstract Alzheimer's disease (AD), a widely studied neurodegenerative disorder, poses
significant research challenges due to its high prevalence and complex etiology. Age, a …
significant research challenges due to its high prevalence and complex etiology. Age, a …