Machine Learning-Empowered Pathology

We developed the first fully-automated algorithm to analyze digital whole-slide histopathology images. Whole-slide histopathology images contain billions of pixels and are difficult to process. To address this challenge, we established image processing modules to identify the regions of interest and extract features describing the size, shape, and pixel intensity distribution of the cell nuclei and cytoplasm. The extracted features from lung cancer histopathology slides successfully predicted patients’ diagnoses and prognoses.


Quantitative Pathology

Linking Pathology with Multi-Omics Profiles

We integrate cancer patients’ quantitative histopathology features with their transcriptomic profiles to identify the associations between molecular pathways and histology morphology of lung adenocarcinoma. These works lay the foundation of quantitative digital pathology analysis.