What file formats does PathML support?

PathML supports over 160 slide formats, including common proprietary types like Aperio SVS, Hamamatsu NDPI, Leica SCN, DICOM, and OME-TIFF.

How does PathML manage large pathology datasets?

It utilizes an HDF5-based storage system to efficiently manage tiles, masks, and extracted features, optimized for high-performance machine learning workflows.

Does this skill include built-in AI models?

PathML integrates pre-built models such as HoVer-Net for simultaneous nucleus segmentation and classification, and HACTNet for hierarchical cell-type classification.

Can PathML handle multiplex imaging data?

Yes, it includes specialized support for multiparametric imaging platforms like CODEX, Vectra, and MERFISH for spatial proteomics and marker quantification.

PathML Computational Pathology

Name: PathML Computational Pathology
Author: ricable

byricable

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데이터 과학 및 ML

Analyzes whole-slide pathology images and multiparametric imaging data using advanced machine learning and spatial graph techniques.

소개

PathML is a specialized toolkit designed for digital pathology workflows, enabling researchers and developers to process over 160 whole-slide image formats. It streamlines complex tasks such as stain normalization, nucleus segmentation, and spatial proteomic analysis from platforms like CODEX and Vectra. By integrating deep learning models like HoVer-Net and providing tools for tissue graph construction, PathML serves as a bridge between raw pathology slides and actionable biological insights or machine learning model training.

주요 기능

Multiparametric imaging analysis for CODEX, Vectra, and spatial proteomics
Integrated nucleus detection and segmentation via HoVer-Net and HACTNet
Spatial tissue graph construction for graph neural network (GNN) applications
1 GitHub stars
Support for 160+ slide formats including Aperio SVS, NDPI, DICOM, and OME-TIFF
Automated H&E preprocessing and Macenko/Vahadane stain normalization

사용 사례

Training deep learning models for automated cancer diagnosis and tissue grading
Quantifying marker expression and cell segmentation in multiplex immunofluorescence images
Performing spatial analysis of cellular relationships within tissue microenvironments

소개

주요 기능

Multiparametric imaging analysis for CODEX, Vectra, and spatial proteomics
Integrated nucleus detection and segmentation via HoVer-Net and HACTNet
Spatial tissue graph construction for graph neural network (GNN) applications
1 GitHub stars
Support for 160+ slide formats including Aperio SVS, NDPI, DICOM, and OME-TIFF
Automated H&E preprocessing and Macenko/Vahadane stain normalization

사용 사례

Training deep learning models for automated cancer diagnosis and tissue grading
Quantifying marker expression and cell segmentation in multiplex immunofluorescence images
Performing spatial analysis of cellular relationships within tissue microenvironments