What image formats does the Histolab skill support?

Histolab supports various pyramidal whole slide image formats common in digital pathology, including SVS, TIFF, and NDPI.

How does Histolab handle artifacts and background noise?

It uses automated tissue masks and morphological filters to identify informative tissue regions and exclude slide background, pen marks, or artifacts.

What tiling strategies are available?

You can use RandomTiler for diverse sampling, GridTiler for full coverage, or ScoreTiler to select tiles based on specific metrics like nuclei density.

Can I use this skill to prepare data for neural network training?

Yes, the skill is specifically designed to extract small tiles from large slides, filtering for tissue content to create clean datasets for deep learning.

Histolab WSI Processing

Name: Histolab WSI Processing
Author: Sologa

bySologa

Ciencia de Datos y ML

Automates tissue detection and tile extraction from whole slide images for digital pathology and machine learning workflows.

Acerca de

Histolab provides a specialized interface for handling gigapixel whole slide images (WSI) in digital pathology pipelines. It simplifies the complex task of identifying tissue regions, filtering out background artifacts, and extracting informative tiles for deep learning model training. By providing multiple tiling strategies—including random, grid, and score-based—and integrated preprocessing filters, it serves as an essential tool for researchers and pathologists building automated analysis systems and preparing high-quality datasets.

Características Principales

Advanced tile scoring based on nuclei density and cellularity
Multi-strategy tile extraction (Random, Grid, and Score-based)
Automated tissue detection and binary masking
Support for multiple WSI formats including SVS, TIFF, and NDPI
Integrated image processing and morphological filters
0 GitHub stars

Casos de Uso

Large-scale spatial analysis and tile-based feature extraction
Automated tissue segmentation and artifact removal in slide images
Preparing image datasets for deep learning pathology models

Acerca de

Características Principales

Advanced tile scoring based on nuclei density and cellularity
Multi-strategy tile extraction (Random, Grid, and Score-based)
Automated tissue detection and binary masking
Support for multiple WSI formats including SVS, TIFF, and NDPI
Integrated image processing and morphological filters
0 GitHub stars

Casos de Uso

Large-scale spatial analysis and tile-based feature extraction
Automated tissue segmentation and artifact removal in slide images
Preparing image datasets for deep learning pathology models