https://github.com/rendeirolab/wsidata

Efficient data structures and IO for whole slide images analysis
https://github.com/rendeirolab/wsidata

deep-learning digital-pathology image-analysis tissue

Last synced: 9 months ago
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Efficient data structures and IO for whole slide images analysis

• Host: GitHub
• URL: https://github.com/rendeirolab/wsidata
• Owner: rendeirolab
• Created: 2024-09-14T10:59:50.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-25T22:30:00.000Z (over 1 year ago)
• Last Synced: 2025-01-25T23:29:57.753Z (over 1 year ago)
• Topics: deep-learning, digital-pathology, image-analysis, tissue
• Language: Python
• Homepage: https://wsidata.rtfd.io/
• Size: 6.06 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# wsidata



    

    

    





  Efficient data structures and IO for whole slide image analysis



[![Documentation Status](https://readthedocs.org/projects/wsidata/badge/?version=latest&style=flat-square)](https://wsidata.readthedocs.io/en/latest)

![pypi version](https://img.shields.io/pypi/v/wsidata?color=0098FF&logo=python&logoColor=white&style=flat-square)

![conda version](https://img.shields.io/conda/vn/conda-forge/wsidata?style=flat-square&logo=anaconda&logoColor=white&color=%2344A833)

![PyPI - License](https://img.shields.io/pypi/l/wsidata?color=FFD43B&style=flat-square)

![scverse ecosystem](https://img.shields.io/badge/scverse_ecosystem-gray.svg?style=flat-square&logo=data:image/svg+xml;base64,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)

wsidata is designed to work with [LazySlide](https://github.com/rendeirolab/LazySlide)

for whole slide image (WSI) analysis.

## Installation

Via pip:

```bash

pip install wsidata

```

Or through conda:

```bash

conda install conda-forge::wsidata

```