Can PathML perform stain normalization?

Yes, PathML includes modular preprocessing pipelines with specific transforms for Macenko and Vahadane stain normalization to standardize H&E images for machine learning.

What deep learning models are integrated with PathML?

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

What file formats does PathML support?

PathML supports over 160 proprietary and open slide formats, including Aperio SVS, Hamamatsu NDPI, DICOM, and OME-TIFF, ensuring compatibility with most digital pathology scanners.

Does this skill support spatial proteomics?

Yes, it provides specialized workflows for analyzing multiparametric data from platforms like CODEX, Vectra, and MERFISH, including cell segmentation and marker quantification.

PathML Computational Pathology

Name: PathML Computational Pathology
Author: x-cmd

byx-cmd

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데이터 과학 및 ML

Analyzes whole-slide images and multiparametric imaging data for computational pathology and machine learning workflows.

PathML is a comprehensive toolkit designed to streamline the analysis of histopathology slides and spatial proteomics data. It enables researchers and developers to load over 160 slide formats, perform complex image preprocessing like stain normalization, and build spatial graphs for cellular relationship analysis. By integrating with deep learning frameworks, this skill facilitates the training and deployment of specialized models like HoVer-Net for nucleus segmentation and HACTNet for cell classification, making it an essential tool for digital pathology and spatial biology research.

주요 기능

01Modular preprocessing pipelines for stain normalization and tissue detection

028 GitHub stars

03Specialized support for multiparametric data from CODEX, Vectra, and MERFISH

04Spatial graph construction for cellular and tissue-level analysis

05Support for 160+ slide formats including Aperio SVS, NDPI, and DICOM

06Automated nucleus detection, segmentation, and classification workflows

사용 사례

01Segmenting and quantifying cell populations in multiplex immunofluorescence images

02Automating the analysis of large-scale histopathology datasets for clinical research

03Training deep learning models for automated disease grading and spatial proteomics

주요 기능

01Modular preprocessing pipelines for stain normalization and tissue detection

028 GitHub stars

03Specialized support for multiparametric data from CODEX, Vectra, and MERFISH

04Spatial graph construction for cellular and tissue-level analysis

05Support for 160+ slide formats including Aperio SVS, NDPI, and DICOM

06Automated nucleus detection, segmentation, and classification workflows

사용 사례

01Segmenting and quantifying cell populations in multiplex immunofluorescence images

02Automating the analysis of large-scale histopathology datasets for clinical research

03Training deep learning models for automated disease grading and spatial proteomics