CellMap: AI-Powered Spatial Cellular Prediction
Bridging the gap between histology and spatial transcriptomics with deep learning
Trusted by Leading Research Institutions
About CellMap
Bridging the gap between standard histology and spatial transcriptomics
Analyzes H&E stained histology slides to extract meaningful spatial information
Predicts cell-type compositions with precise spatial resolution
Leverages state-of-the-art CNN architectures for accurate predictions
Predicts 35 different cell types from a single histology image
Fully open-source implementation with comprehensive documentation
Optimized data processing pipeline for large histology images
Model Architecture
Our deep learning approach for spatial cellular prediction
CellMap Model Architecture
Predicting cell-type compositions from H&E stained histology slides
Key Components
- Data Preprocessing: H&E stain normalization and patch extraction
- Feature Extraction: CNN backbone (ResNet50, EfficientNet, DenseNet)
- Regression Head: Multi-output prediction for 35 cell types
- Loss Function: Combined MSE and Spearman correlation loss
Results
Visualizing our model's performance and predictions
Spearman Correlation
Spearman Correlation: 0.87
Average across all cell types
Average Spearman correlation: 0.87 across all cell types
Model Comparison
Our model outperforms baseline methods by 15-20%
Performance by Cell Type
Performance varies by cell type, with highest accuracy for abundant cell types
Interactive Demo
Experience CellMap in action with our interactive demo
Upload a histology image to see CellMap's predictions
The full demo requires server-side processing with our trained model.
Our Team
The minds behind CellMap
Expert in computational biology and deep learning with 10+ years of experience in spatial transcriptomics.
Specialist in computer vision and deep learning models with expertise in medical image analysis.
Ready to Get Started?
Join us in advancing spatial cellular prediction with CellMap