Is it suitable for deep learning dataset preparation?

Absolutely; it is specifically designed to prepare large-scale, high-quality tile datasets for training convolutional neural networks (CNNs) in computational pathology.

What are the different tile extraction strategies available?

It offers RandomTiler for diverse sampling, GridTiler for complete slide coverage, and ScoreTiler to prioritize tiles with specific features like high nuclei density.

What image formats does Histolab support?

Histolab supports common Whole Slide Image (WSI) formats including .svs, .tiff, and .ndpi, utilizing OpenSlide compatibility for high-resolution processing.

Does it support custom image filters?

Yes, you can compose custom filter pipelines including grayscale conversion, Otsu thresholding, and morphological operations to suit specific tissue types.

Can I filter out background or artifacts automatically?

Yes, the skill provides automated tissue detection masks like TissueMask and BiggestTissueBoxMask to ensure only relevant biological areas are processed.

Histolab Pathology Processing

Name: Histolab Pathology Processing
Author: BbgnsurfTech

byBbgnsurfTech

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데이터 과학 및 ML

Automates digital pathology image processing and tile extraction for whole slide images in deep learning pipelines.

Histolab is a specialized Claude Code skill designed for researchers and data scientists working in digital pathology. It provides a robust framework for handling gigapixel whole slide images (WSI), enabling automated tissue detection, sophisticated image filtering, and flexible tile extraction strategies including Random, Grid, and Score-based methods. Whether you are preparing datasets for histological deep learning models or performing spatial analysis on SVS, TIFF, or NDPI files, this skill streamlines the preprocessing workflow and ensures high-quality input data for computational pathology applications.

주요 기능

013 GitHub stars

02High-performance tile extraction using Grid, Random, and Scored strategies

03Advanced image preprocessing filters for color normalization and nuclei enhancement

04Automated tissue detection and mask generation to exclude background artifacts

05Built-in visualization tools for slide thumbnails, masks, and tile locations

06Comprehensive support for WSI formats including SVS, TIFF, and NDPI

사용 사례

01Automating tissue-to-background segmentation and artifact removal in pathology slides

02Extracting high-informational regions of interest (ROI) based on nuclei density scoring

03Preparing large-scale datasets for cancer detection and classification models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bbgnsurftech/claude-skills-collection histolab

For use in Claude.ai and ChatGPT

Download Skill