How does Histolab help with deep learning datasets?

It automates the extraction of small, manageable tiles from gigapixel images, ensuring that only tissue-containing regions are used for training.

What slide formats does the Histolab skill support?

Histolab supports common Whole Slide Image (WSI) formats including SVS, TIFF, and NDPI, handling pyramidal structures efficiently.

Can I filter out artifacts like pen marks or background?

Yes, Histolab includes specialized masks and filter compositions to detect tissue and exclude non-informative regions or artifacts.

What is the difference between GridTiler and ScoreTiler?

GridTiler provides systematic coverage of the entire tissue area, while ScoreTiler uses functions like NucleiScorer to pick the most informative regions.

Histolab Pathology Toolkit

Name: Histolab Pathology Toolkit
Author: ricable

byricable

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Data Science & ML

Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.

About

Histolab is a specialized digital pathology skill that simplifies the complex task of handling gigapixel Whole Slide Images (WSI). It provides robust tools for automated tissue detection, background/artifact exclusion, and extracting meaningful tiles through various strategies like random sampling, systematic grid coverage, or score-based selection based on nuclei density. By standardizing the preprocessing of large-scale formats like SVS and TIFF, this skill enables researchers and developers to efficiently build high-quality datasets for computational pathology and histological analysis within the Claude environment.

Key Features

Comprehensive support for gigapixel WSI formats such as SVS, TIFF, and NDPI
Integrated visualization for slide thumbnails, masks, and tile locations
Advanced image preprocessing for pen removal and nuclei enhancement
1 GitHub stars
Multi-strategy tile extraction including Grid, Random, and Score-based methods
Automated tissue detection and mask generation using advanced filtering

Use Cases

Preparing histological datasets for training deep learning models
Performing systematic spatial analysis across whole tissue sections
Identifying and extracting high-cellularity regions for diagnostic research

About

Key Features

Comprehensive support for gigapixel WSI formats such as SVS, TIFF, and NDPI
Integrated visualization for slide thumbnails, masks, and tile locations
Advanced image preprocessing for pen removal and nuclei enhancement
1 GitHub stars
Multi-strategy tile extraction including Grid, Random, and Score-based methods
Automated tissue detection and mask generation using advanced filtering

Use Cases

Preparing histological datasets for training deep learning models
Performing systematic spatial analysis across whole tissue sections
Identifying and extracting high-cellularity regions for diagnostic research