Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data.

ABSTRACT: Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning mod

SUBMITTER: Sui Z 

PROVIDER: S-EPMC11326158 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature