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.
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About pathml
PathML is a powerful computational pathology toolkit designed for the analysis of whole-slide images (WSI) and multiparametric imaging data. It addresses the challenges faced by researchers and clinicians in the field of histopathology by providing tools to efficiently load, preprocess, and analyze complex imaging data. By supporting over 160 slide formats, including industry standards such as Aperio SVS and DICOM, PathML enables seamless integration into existing digital pathology workflows, ensuring that users can focus on extracting meaningful insights from their data rather than wrestling with compatibility issues.
FAQ
What types of images can I load using PathML?
PathML supports over 160 slide formats including Aperio SVS, NDPI, DICOM, and OME-TIFF.
Is PathML suitable for deep learning model training?
Yes, PathML integrates with PyTorch and provides pre-built models like HoVer-Net and HACTNet for training and inference.
Can I preprocess H&E stained images with PathML?
Absolutely, PathML includes modules for stain normalization and tissue detection specifically for H&E stained images.
Does PathML support multiplex immunofluorescence analysis?
Yes, PathML is equipped to analyze data from multiplex imaging platforms like CODEX and Vectra.
What are the system requirements for using PathML?
PathML requires Python and compatible libraries for image processing and machine learning, though specific requirements may vary based on the features used.
Install pathml
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
davila7/claude-code-templates