pathml

Topics in the source

• PathML
• Overview
• When to Use This Skill
• Core Capabilities
• 1. Image Loading & Formats
• 2. Preprocessing Pipelines
• 3. Graph Construction
• 4. Machine Learning

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. 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 Analyzing multiparametric imaging (CODEX, Vectra, MERFISH) for spatial proteomics Quantifying marker expression from multiplex immunofluorescence Managing large-scale pathology datasets with HDF5 storage Tile-based analysis and stitching operations PathML provides six major capability areas documented in detail within reference files: Load whole-slide images from 160+ proprietary format

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