Fusheng Wang

Cell type populations for 3D anatomical structures of the Human Reference Atlas

The paper quantifies cell type populations across 3D anatomical structures using single-cell transcriptomics and proteomics data from the Human Reference Atlas.

Specimen, Biological Structure, and Spatial Ontologies in Support of a Human Reference Atlas

This paper introduces the Common Coordinate Framework Ontology (CCFO) v2.0.1 to integrate specimen, biological structure, and spatial data for the Human Reference Atlas, enabling interoperability and programmatic access.

Deep Learning Based Registration of Serial Whole-slide Histopathology Images in Different Stains

This paper introduces CycGANRegNet, a deep learning method for registering serial WSIs in different stains, achieving better performance than existing methods on breast cancer patient data.

Real-Time Spatial Registration for 3D Human Atlas

This paper introduces a framework for real-time 3D tissue block registration in a human atlas, demonstrating its practicality and efficiency through experiments.

Artificial Intelligence based Liver Portal Tract Region Identification and Quantification with Transplant Biopsy Whole-Slide Images

This paper develops a deep learning model to segment portal tract regions in liver biopsy images, showing strong correlation with fibrosis staging.

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

The paper introduces DPLab, a web-based platform for analyzing whole-slide pathology images, enabling 3D tissue reconstruction and sharing of analysis results.

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

This paper develops a deep learning system to predict pathological complete response in TNBC patients using histopathology images and biomarkers, achieving 93% accuracy.

MultiHeadGAN: A Deep Learning Method for Low Contrast Retinal Pigment Epithelium Cells Segmentation in Fluorescent Flatmount Microscopy Images

The paper introduces MultiHeadGAN, a deep learning method for segmenting low contrast RPE cells in impaired flatmount images, achieving high segmentation accuracy.