IEEE

Hybrid ResNet-ViT Transfer Learning Approach for Brain Stroke Classification on Computed Tomography Images

Abstract: This study investigates the utilization of a hybrid Convolutional Neural Network (CNN) and Vision Transformer (ViT) model, employing transfer learning methods, to enhance brain stroke ...
News-Medical.Net

AI trained on sleep data predicts future disease and mortality years in advance

The SleepFM model reveals how sleep analysis can predict disease risk, offering insights into sleep's role as a vital health ...
IEEE

Hybrid CNN-Based Transfer Learning Enhances Brain Tumor Classification on MRI Images

Abstract: Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...
AOL

The Definition of Transfer learning

Transfer learning is a machine learning technique that allows a model trained on one task to be repurposed or fine-tuned for a related task, drastically reducing the amount of data and computational ...
Sports Illustrated

REPORT: UCLA Transfer's Success Story Example of Transfer Portal Benefit

An analyst recently referred to Donovan Dent's commitment to UCLA as an example of why the transfer portal can be a very good thing. There's a lot of criticism surrounding the transfer portal, which, ...
Frontiers

Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients

Introduction: Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains ...