Classification of Alzheimer's Disease Based on Amyloid-PET using Random Forest Ensemble

A Comparative and Summative Study of Radiomics-Based Overall Survival Prediction in Glioblastoma Patients

Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Artificial Functional MRI Data

Deep learning-based Cerebral Microbleeds detection on quantitative susceptibility mapping(QSM) for Stroke Cohort

DL-BET - A deep learning based tool for automatic brain extraction from structural magnetic resonance images in mice.

Image-to-image translation of 3T to 7T MRI using Generative Adversarial Networks: A step towards longitudinal harmonization

Improved Automated Hippocampus Segmentation using Deep Neural Networks

Improved Segmentation of MR Brain Images by Integrating Bayesian and Deep Learning-Based Classification

Improving the generalizability of convolutional neural networks for T2-lesion segmentation of gliomas in the post-treatment setting

Increasing Feature Sparsity in Alzheimer's Disease Classification with Relevance-Guided Deep Learning

Mapping transient coactivity patterns of brain in latent space with variational autoencoder neural network

Author:Kaiming Li  Xiaoping Hu  

Session Type:Digital Poster  

Session Date:Wednesday, 19 May 2021  

Topic:Emerging Applications of AI in Neuroimaging  

Session Name:Emerging Applications of AI in Neuroimaging for CES I  

Program Number:3497  

Room Session:Concurrent 6  

Institution:UC Riverside  

Preoperative MR Radiomics and ADC Value for Prediction of Progression and Recurrence in Meningiomas

Preoperative MR Radiomics for Prediction of Progression and Recurrence in Non-functional Pituitary Macroadenomas

Self-Supervised Transfer Learning for Infant Cerebellum Segmentation with Multi-Domain MRIs

A semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual labels

Simulation of brain deficits on MRI: A novel approach of 'ground truth' generation for machine learning

White matter hyperintensity volumes and cognition: Assessment of a deep learning-based lesion detection and quantification algorithm on ADNI