Which tiling strategy is best for my dataset?

Use RandomTiler for diverse sampling during model training, GridTiler for complete spatial coverage and slide reconstruction, or ScoreTiler to extract tiles based on high nuclei density or cellularity.

How does Histolab handle background and artifacts?

Histolab uses advanced filters and classes like TissueMask to automatically generate binary masks that identify tissue regions, allowing the extractors to ignore background, pen annotations, and slide artifacts.

What image formats does the Histolab skill support?

Histolab supports common whole slide image (WSI) formats including SVS, TIFF, and NDPI through its underlying slide management system, handling pyramidal image structures automatically.

Is this skill suitable for multiplexed imaging?

Histolab is optimized for lightweight, tile-based H&E slide processing. For more complex workflows involving spatial proteomics or multiplexed imaging, pathml is generally the preferred alternative.

Histolab Pathology Imaging

Name: Histolab Pathology Imaging
Author: youyinnn

byyouyinnn

0•

データサイエンスとML

Automates tissue detection and tile extraction from high-resolution whole slide images for digital pathology pipelines.

Histolab is a specialized library for digital pathology that streamlines the complex process of handling gigapixel whole slide images (WSI). It provides automated tissue segmentation to filter out background artifacts, multiple tiling strategies—including Random, Grid, and Score-based—for robust dataset creation, and a comprehensive suite of preprocessing filters. By simplifying WSI management and tile-based analysis, this skill allows researchers and developers to efficiently prepare high-quality datasets for deep learning models without manual image processing overhead.

主な機能

01Automated tissue detection and binary mask generation

02Integrated visualization tools for slide thumbnails and tile locations

030 GitHub stars

04Advanced morphological filters and image preprocessing pipelines

05Support for gigapixel WSI formats like SVS, TIFF, and NDPI

06Multi-strategy tile extraction (Random, Grid, and Score-based)

ユースケース

01High-nuclear-density region identification and extraction for analysis

02Automated tissue segmentation and background removal in gigapixel images

03Preparing large-scale datasets for deep learning in pathology

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection histolab

For use in Claude.ai and ChatGPT

主な機能

01Automated tissue detection and binary mask generation

02Integrated visualization tools for slide thumbnails and tile locations

030 GitHub stars

04Advanced morphological filters and image preprocessing pipelines

05Support for gigapixel WSI formats like SVS, TIFF, and NDPI

06Multi-strategy tile extraction (Random, Grid, and Score-based)

ユースケース

01High-nuclear-density region identification and extraction for analysis

02Automated tissue segmentation and background removal in gigapixel images

03Preparing large-scale datasets for deep learning in pathology

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection histolab

For use in Claude.ai and ChatGPT