Ge Huang

The paper develops a deep learning framework to identify and quantify rare hybrid cells in pancreatic cancer models using fluorescence microscopy.

This paper develops BBB-permeable oxazine fluorophores for real-time intraoperative imaging of white matter tracts in neurosurgery.

This study identifies RUNX1 as a key driver of tumor-immune hybrid cell invasion and dissemination in colorectal cancer, highlighting circulating hybrid cells as a biomarker for metastasis.

This study tracks single nanoMOFs in live cells to understand their intracellular dynamics and internalization pathways using fluorescence and machine learning.

The paper introduces a deep learning tool for automated histologic feature quantification in murine pancreatic disease models, showing high correlation with immunostaining results and accurate differentiation of disease states.