6 citations · 18 across the 13 of their papers we have counts for
9 papers · 1 filter
BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
Yifei Sun, Zhanghao Chen, Hao Zheng +7
Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to its accessibility and affordability. Nonetheless, the detec…
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis
Xinyuan Shi, Fangfang Zhu, Wenwen Min
Emerging evidence indicates that human cancers are intricately linked to human microbiomes, forming an inseparable connection. However, due to limited sample sizes and significant…
Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net
Donghai Fang, Fangfang Zhu, Wenwen Min
With the rapid development of the latest Spatially Resolved Transcriptomics (SRT) technology, which allows for the mapping of gene expression within tissue sections, the integrativ…
Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data
Donghai Fang, Fangfang Zhu, Dongting Xie +1
With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial conte…
High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE
Zhiceng Shi, Shuailin Xue, Fangfang Zhu +1
Spatial transcriptomics (ST) is a groundbreaking genomic technology that enables spatial localization analysis of gene expression within tissue sections. However, it is significant…
SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
Xiaoyu Li, Fangfang Zhu, Wenwen Min
The rapid development of spatial transcriptomics (ST) technologies is revolutionizing our understanding of the spatial organization of biological tissues. Current ST methods, categ…
stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement
Shuailin Xue, Fangfang Zhu, Changmiao Wang +1
The spatial location of cells within tissues and organs is crucial for the manifestation of their specific functions.Spatial transcriptomics technology enables comprehensive measur…
Multimodal contrastive learning for spatial gene expression prediction using histology images
Wenwen Min, Zhiceng Shi, Jun Zhang +2
In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within…
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
Xiaoyu Li, Wenwen Min, Shunfang Wang +2
Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. Ho…