Computational Pathology Agent

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---
name: computational-pathology-agent
description: Analyze Whole Slide Images (WSI) for digital pathology, including tissue segmentation and feature extraction.
keywords:
  - wsi
  - digital-pathology
  - deep-learning
  - resnet
  - openslide
measurable_outcome: Preprocess and extract tissue patches from a 1GB+ .svs slide within 15 minutes for downstream ML tasks.
license: MIT
metadata:
  author: MD BABU MIA, PhD
  version: "1.0.0"
compatibility:
  - system: python 3.9+
allowed-tools:
  - run_shell_command
  - read_file
  - write_file
---

# Computational Pathology Agent

**Version:** 1.0.0
**Author:** MD BABU MIA, PhD
**Date:** February 2026

## Overview
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.

## Capabilities
1.  **WSI Handling:** Efficient reading/tiling of .svs, .ndpi, .tiff files (using OpenSlide/TiffSlide).
2.  **Tissue Segmentation:** Separation of tissue from background.
3.  **Patch Extraction:** Automated generation of patches for ML training/inference.
4.  **Nuclei Segmentation:** Integration with StarDist/HoverNet for cellular analysis.
5.  **Feature Extraction:** Generating feature vectors for slide-level clustering.
6.  **MMR/MSI Status Prediction:** Predict mismatch-repair (dMMR) status from colorectal cancer histopathology. Per Petäinen et al. (Comput Methods Programs Biomed, Jun 2026, PMID 41875848), leverage non-tumor regions and low-magnification whole-slide context — not only tumor tiles — to boost dMMR prediction accuracy.

## Usage
```python
from 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()
```

## Requirements
*   openslide-python
*   opencv-python
*   pytorch
*   scikit-image

## References
- Petäinen L, Väyrynen JP, Böhm J, Ruusuvuori P, Ahtiainen M. dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions. Comput Methods Programs Biomed. 2026 Jun. https://pubmed.ncbi.nlm.nih.gov/41875848/

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