José Luis Ambite

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.

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.

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.

Selecting Clustering Algorithms for IBD Mapping

The paper evaluates clustering algorithms for IBD mapping, finding that Markov Clustering outperforms others in statistical power and efficiency.

Rapid detection of identity-by-descent tracts for mega-scale datasets

The paper introduces iLASH, an algorithm for fast and accurate detection of identity-by-descent tracts in large genomic datasets, achieving significant speed improvements over existing methods.

Towards a Fine-scale Population Health Monitoring System

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

This paper introduces NERO, a biomedical named-entity ontology with an annotated corpus, and demonstrates its utility through text embedding for discovering associations.