Xin Yuan

Ultra-efficient High Resolution 3D Reconstruction of Spatial Omics Data with Neural Transcriptomic Field

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.

Features fusion or not: harnessing multiple pathological foundation models using Meta-Encoder for downstream tasks fine-tuning

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

Unveiling Fine-scale Spatial Structures and Amplifying Gene Expression Signals in Ultra-Large ST slices with HERGAST

HERGAST identifies spatial structures and amplifies gene expression signals in ultra-large spatial transcriptomics data, outperforming existing methods and revealing novel biological insights in cancer.

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

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: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic data

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