Greg Ver Steeg

Generating diffusion MRI scalar maps from T1-weighted images using Reversible GANs

This paper generates synthetic diffusion MRI scalar maps from T1-weighted images using RevGAN, showing they retain microstructural features and perform well in downstream tasks.

Synthetic Diffusion Tensor Imaging Maps Generated by 2D and 3D Probabilistic Diffusion Models: Evaluation and Applications

This study evaluates 2D and 3D diffusion models for generating synthetic DTI maps, finding 3D models perform better in downstream tasks like sex and dementia classification.

Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification

This study compares explainable AI models for Alzheimer's disease classification using MRI scans, evaluating interpretability methods and generalization across different populations.

UNSUPERVISED HARMONIZATION OF BRAIN MRI USING 3D CYCLE GANS AND ITS EFFECT ON BRAIN AGE PREDICTION

This paper harmonizes brain MRI data from multiple sources using 3D CycleGANs and shows improved accuracy in brain age prediction compared to raw data.

Evaluation of Transfer Learning Methods for Detecting Alzheimer's Disease with Brain MRI

This paper evaluates transfer learning methods for Alzheimer's disease classification using brain MRI, finding that contrastive learning improves performance, especially with limited data.

Predicting Dementia Severity by Merging Anatomical and Diffusion MRI with Deep 3D Convolutional Neural Networks

This paper uses deep 3D CNNs combining anatomical and diffusion MRI to predict dementia severity and brain age, finding that diffusion MRI improves performance over anatomical MRI alone.

3D Convolutional Neural Networks for Classification of Alzheimer's and Parkinson's Disease with T1-Weighted Brain MRI

This paper proposes a 3D CNN for classifying Alzheimer's and Parkinson's disease using T1-weighted MRI, achieving higher ROC-AUC than random forest on multiple datasets.