bibtext: @InProceedings{ Mej_Enhancing_MICCAI2024,
                   author = { Mejia, Gabriel and Ruiz, Daniela and Cárdenas, Paula and Manrique, Leonardo and Vega, Daniela and Arbeláez, Pablo },
                   title = { { Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture yielding high corruption in acquired data. Given potential applications, recent efforts have focused on predicting transcriptomic profiles solely from histology images. However, differences in databases, preprocessing techniques, and training hyperparameters hinder a fair comparison between methods. To address these challenges, we present a systematically curated and processed database collected from 26 public sources, representing an 8.6-fold increase compared to previous works. Additionally, we propose a state-of-the-art transformer-based completion technique for inferring missing
  gene expression, which significantly boosts the performance of transcriptomic profile predictions across all datasets. Altogether, our contributions constitute the most comprehensive benchmark of gene expression prediction from histology images to date and a stepping stone for future research on spatial transcriptomics.
title: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion
authors: Mejia, Gabriel Ruiz, Daniela Cárdenas, Paula Manrique, Leonardo Vega, Daniela Arbeláez, Pablo