1 citations · 1 across the 9 of their papers we have counts for
9 papers
Correlation-Guided Flow Matching with Annealed Masking for Spatial Transcriptomics Generation
Yupei Zhang, Hao Chen, Li Pan +2
Spatial transcriptomics (ST) provides spatially resolved gene expression profiling but remains expensive, motivating the prediction of ST from histology images. Generative models h…
C3-Diff: Super-resolving Spatial Transcriptomics via Cross-modal Cross-content Contrastive Diffusion Modelling
Xiaofei Wang, Stephen Price, Chao Li
The rapid advancement of spatial transcriptomics (ST), i.e., spatial gene expressions, has made it possible to measure gene expression within original tissue, enabling us to discov…
Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization
Yupei Zhang, Xiaofei Wang, Anran Liu +2
Histopathology remains the gold standard for cancer diagnosis and prognosis. With the advent of transcriptome profiling, multi-modal learning combining transcriptomics with histolo…
Score-based Diffusion Model for Unpaired Virtual Histology Staining
Anran Liu, Xiaofei Wang, Jing Cai +1
Hematoxylin and eosin (H&E) staining visualizes histology but lacks specificity for diagnostic markers. Immunohistochemistry (IHC) staining provides protein-targeted staining but i…
Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling
NingFeng Que, Xiaofei Wang, Jingjing Chen +2
Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecu…
Joint Modelling Histology and Molecular Markers for Cancer Classification
Xiaofei Wang, Hanyu Liu, Yupei Zhang +6
Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Al…
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning
Yupei Zhang, Xiaofei Wang, Fangliangzi Meng +2
Multi-modal learning plays a crucial role in cancer diagnosis and prognosis. Current deep learning based multi-modal approaches are often limited by their abilities to model the co…
Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology
Huahui Yi, Xiaofei Wang, Kang Li +1
Multimodal learning, integrating histology images and genomics, promises to enhance precision oncology with comprehensive views at microscopic and molecular levels. However, existi…
Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics
Xiaofei Wang, Xingxu Huang, Stephen J. Price +1
The recent advancement of spatial transcriptomics (ST) allows to characterize spatial gene expression within tissue for discovery research. However, current ST platforms suffer fro…