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Get Started Free →Analyze Whole Slide Images (WSI) for digital pathology, including tissue segmentation and feature extraction.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -37% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -39% | 0% |
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Version: 1.0.0 Author: MD BABU MIA, PhD Date: February 2026
This agent specializes in the analysis of Whole Slide Images (WSIs) for digital pathology. It leverages Deep Learning models (ResNet, ViT, HoverNet) to perform segmentation, classification, and feature extraction from gigapixel histology images.
pythonfrom Skills.Pathology_AI.Computational_Pathology_Agent.wsi_analyzer import WSIAnalyzer # Initialize path_agent = WSIAnalyzer(slide_path="./data/biopsy_001.svs") # Extract tissue patches path_agent.extract_patches(patch_size=256, level=1) # Analyze Nuclei (requires model weights) # path_agent.segment_nuclei()
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