4 citations · 6 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…
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…
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
Tobias Rueckert, David Rauber, Raphaela Maerkl +58
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…
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…