2 citations · 3 across the 4 of their papers we have counts for
5 papers
Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics
Daniela Vega, Paula Cárdenas, Hannah Ceballos +2
Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, high operational costs, special…
CARDIUM: Congenital Anomaly Recognition with Diagnostic Images and Unified Medical records
Daniela Vega, Hannah V. Ceballos, Javier S. Vera +10
Prenatal diagnosis of Congenital Heart Diseases (CHDs) holds great potential for Artificial Intelligence (AI)-driven solutions. However, collecting high-quality diagnostic data rem…
Latent Gene Diffusion for Spatial Transcriptomics Completion
Paula Cárdenas, Leonardo Manrique, Daniela Vega +2
Computer Vision has proven to be a powerful tool for analyzing Spatial Transcriptomics (ST) data. However, current models that predict spatially resolved gene expression from histo…
Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking
Daniela Ruiz, Paula Cárdenas, Leonardo Manrique +3
Spatial Transcriptomics is a groundbreaking technology that integrates histology images with spatially resolved gene expression profiles. Among the various Spatial Transcriptomics…
SpaRED benchmark: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion
Gabriel Mejia, Daniela Ruiz, Paula Cárdenas +3
Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture…