Can I use this skill for nucleus segmentation?

Yes, it includes specialized tools and integrated models like HoVer-Net and HACTNet for simultaneous nucleus detection, segmentation, and classification.

What image formats does the PathML skill support?

PathML supports over 160 proprietary and open formats including Aperio SVS, Hamamatsu NDPI, Leica SCN, DICOM, and OME-TIFF, providing a unified interface for access.

How does PathML handle large pathology datasets?

It utilizes HDF5 storage and tile-based processing to efficiently manage large-scale data, masks, and metadata optimized for machine learning training.

Does PathML support spatial proteomics?

Yes, it provides dedicated workflows for multiparametric imaging platforms like CODEX, Vectra, and MERFISH, including marker quantification and AnnData integration.

PathML Pathology Analysis

Name: PathML Pathology Analysis
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Analyzes whole-slide pathology images and multiparametric data using specialized computational workflows and machine learning.

About

PathML is a comprehensive Python-based toolkit designed for advanced computational pathology workflows, enabling researchers and developers to process, analyze, and model large-scale histological data. It provides seamless support for over 160 whole-slide image formats, modular preprocessing pipelines for stain normalization and tissue detection, and specialized tools for spatial analysis through tissue graph construction. Whether you are training deep learning models like HoVer-Net for nucleus segmentation or analyzing complex multiparametric data from platforms like CODEX, PathML streamlines the transition from raw image data to actionable biological insights.

Key Features

Multiparametric imaging support for CODEX, Vectra, and spatial proteomics
Deep learning integration for nucleus segmentation and classification
Spatial graph construction for cell and tissue-level relationship analysis
1 GitHub stars
Support for 160+ slide formats including Aperio SVS, NDPI, and OME-TIFF
Advanced preprocessing for H&E stain normalization and tissue detection

Use Cases

Training and deploying ML models for nucleus detection and segmentation
Building high-performance pipelines for large-scale digital pathology datasets
Quantifying marker expression in multiplex immunofluorescence data

About

Key Features

Multiparametric imaging support for CODEX, Vectra, and spatial proteomics
Deep learning integration for nucleus segmentation and classification
Spatial graph construction for cell and tissue-level relationship analysis
1 GitHub stars
Support for 160+ slide formats including Aperio SVS, NDPI, and OME-TIFF
Advanced preprocessing for H&E stain normalization and tissue detection

Use Cases

Training and deploying ML models for nucleus detection and segmentation
Building high-performance pipelines for large-scale digital pathology datasets
Quantifying marker expression in multiplex immunofluorescence data