computational-pathology-agent

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Install this agent skill to your Project

npx add-skill https://github.com/beita6969/ScienceClaw/tree/main/skills/computational-pathology-agent

SKILL.md

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

WSI Handling: Efficient reading/tiling of .svs, .ndpi, .tiff files (using OpenSlide/TiffSlide).
Tissue Segmentation: Separation of tissue from background.
Patch Extraction: Automated generation of patches for ML training/inference.
Nuclei Segmentation: Integration with StarDist/HoverNet for cellular analysis.
Feature Extraction: Generating feature vectors for slide-level clustering.

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()