Saket S. Ozarkar
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View article: Parameter Efficient Fine-tuning of Transformer-based Masked Autoencoder Enhances Resource Constrained Neuroimage Analysis
Parameter Efficient Fine-tuning of Transformer-based Masked Autoencoder Enhances Resource Constrained Neuroimage Analysis Open
Recent innovations in artificial intelligence (AI) have increasingly focused on large-scale foundational models that are more general purpose in contrast to conventional models trained to perform specialized tasks. Transformer-based archit…
View article: Deep Learning Algorithms for Alzheimer’s Disease Detection based on Diffusion MRI: Tests in Indian and North American Cohorts
Deep Learning Algorithms for Alzheimer’s Disease Detection based on Diffusion MRI: Tests in Indian and North American Cohorts Open
Background Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer’s disease or infer dementia severity from T1‐weighted brain MRI. Here we tested the added value of incorporating information…
View article: Evaluating Synthetic Diffusion MRI Maps created with Diffusion Denoising Probabilistic Models
Evaluating Synthetic Diffusion MRI Maps created with Diffusion Denoising Probabilistic Models Open
Generative AI models, such as Stable Diffusion, DALL-E, and MidJourney, have recently gained widespread attention as they can generate high-quality synthetic images by learning the distribution of complex, high-dimensional image data. Thes…
View article: Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer’s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI
Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer’s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI Open
Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer’s disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As ne…
View article: Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts
Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts Open
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer’s disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMR…
View article: Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis Open
Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of …
View article: Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs*
Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs* Open
Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer’s disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, A…