What slide formats can the Histolab skill handle?

The Histolab skill supports common digital pathology formats including SVS, TIFF, and NDPI, typically used in gigapixel whole slide imaging.

What is the difference between the available tiling strategies?

RandomTiler extracts diverse samples for training, GridTiler ensures complete slide coverage, and ScoreTiler selects tiles based on biological importance like nuclei density.

How does this skill help with deep learning training?

It provides optimized workflows for extracting small, high-resolution tiles from massive slide images, which are essential for training neural networks in computational pathology.

Can I filter out background or non-tissue areas automatically?

Yes, the skill utilizes TissueMask and BiggestTissueBoxMask to automatically detect tissue regions and exclude artifacts or empty background space.

Histolab Digital Pathology Toolkit

Name: Histolab Digital Pathology Toolkit
Author: Microck

byMicrock

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Ciencia de Datos y ML

Processes digital pathology whole slide images by automating tissue detection, tile extraction, and preprocessing for machine learning pipelines.

Acerca de

Histolab is a specialized library for computational pathology designed to handle the immense scale of gigapixel whole slide images (WSI). This Claude Code skill provides developers and researchers with domain-specific guidance for loading formats like SVS and NDPI, implementing automated tissue segmentation through advanced masking, and executing sophisticated tile extraction strategies. It is particularly useful for building robust deep learning datasets, offering built-in tools for nuclei scoring, color space filtering, and morphological preprocessing to ensure only high-quality, informative tissue regions are analyzed.

Características Principales

Versatile tile extraction via Random, Grid, and Score-based tilers
Automated tissue detection and artifact exclusion masking
Multi-format WSI support including SVS, TIFF, and NDPI files
Nuclei-based scoring for identifying informative regions of interest
81 GitHub stars
Comprehensive preprocessing filters for image normalization and enhancement

Casos de Uso

Standardizing digital pathology workflows for consistent tissue region extraction
Automating the preprocessing of large-scale H&E or IHC stained slide cohorts
Generating high-quality image datasets for training oncology deep learning models

Acerca de

Características Principales

Versatile tile extraction via Random, Grid, and Score-based tilers
Automated tissue detection and artifact exclusion masking
Multi-format WSI support including SVS, TIFF, and NDPI files
Nuclei-based scoring for identifying informative regions of interest
81 GitHub stars
Comprehensive preprocessing filters for image normalization and enhancement

Casos de Uso

Standardizing digital pathology workflows for consistent tissue region extraction
Automating the preprocessing of large-scale H&E or IHC stained slide cohorts
Generating high-quality image datasets for training oncology deep learning models