Ruitian Gao

This paper introduces NTF, a deep learning framework for high-resolution 3D reconstruction of spatial omics data, achieving fast and scalable results across multiple species and datasets.

The paper introduces Meta-Encoder, a framework that combines multiple pathological foundation models to improve downstream task performance in computational pathology.

This paper introduces BEPH, a lightweight foundation model for cancer diagnosis and survival prediction from histopathological images, showing improved performance over existing models with limited data.

HEARTSVG identifies spatially variable genes in spatial transcriptomic data with high accuracy and efficiency, uncovering biologically significant patterns in colorectal cancer.

The paper introduces an IGI-DL model that predicts gene spatial expression from HE images and improves cancer prognosis using a risk score derived from predicted gene expression.