Individual skill
pathml
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
cli-tool/components/skills/scientific/pathml/SKILL.md
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- Last checked
- Nov 4, 2026
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- Repository last updated
- Nov 7, 2026
- License
- MIT
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SKILL.mdcore · 7,429 bytesSKILL.mdcore · 7,429 bytesSKILL.mdcore · 7,429 bytesreferences/data_management.mddoc · 19,018 bytesreferences/graphs.mddoc · 16,726 bytesreferences/image_loading.mddoc · 12,146 bytesreferences/machine_learning.mddoc · 19,946 bytesreferences/multiparametric.mddoc · 18,199 bytesreferences/preprocessing.mddoc · 18,209 bytes
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# PathML
## Overview
PathML is a comprehensive Python toolkit for computational pathology workflows, designed to facilitate machine learning and image analysis for whole-slide pathology images. The framework provides modular, composable tools for loading diverse slide formats, preprocessing images, constructing spatial graphs, training deep learning models, and analyzing multiparametric imaging data from technologies like CODEX and multiplex immunofluorescence.
## When to Use This Skill
Apply this skill for:
- Loading and processing whole-slide images (WSI) in various proprietary formats
- Preprocessing H&E stained tissue images with stain normalization
- Nucleus detection, segmentation, and classification workflows
- Building cell and tissue graphs for spatial analysis
- Training or deploying machine learning models (HoVer-Net, HACTNet) on pathology data