Ruichen Lab

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Deep learning-based H-score quantification of immunohistochemistry-stained images

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

Unsupervised domain adaptation for nuclei segmentation: Adapting from hematoxylin & eosin stained slides to immunohistochemistry stained slides using a curriculum approach

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning System

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data

Systems and methods for characterizing a tumor microenvironment using pathological images

Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images

Enhanced Pathology Image Quality with Restore–Generative Adversarial Network

Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung cancer

MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning

MB-GAN: microbiome simulation via generative adversarial network

HiddenVis: a Hidden State Visualization Toolkit to Visualize and Interpret Deep Learning Models for Time Series Data

Artificial intelligence in lung cancer pathology image analysis