Home› Projects› HistomicsTK
HistomicsTK
Python

HistomicsTK

A Python toolkit for pathology image analysis algorithms.

by DigitalSlideArchive · GitHub
Stars
Forks
License
Created
Last commit
Language
bioimage-informaticscomputer-visiondigital-slide-archiveApache-2.0Python
View on GitHub
In plain words

Analyze pathology images using a toolkit designed for medical image processing.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
HistomicsTK — GitHub preview card
📈 Star history
480479
2026-07-202026-08-31
📈 Track HistomicsTK

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

A Python toolkit for pathology image analysis algorithms.

HistomicsTK has 479 stars on GitHub. It has been forked 128 times. HistomicsTK is written mainly in Python. It has been in active development since 2015. HistomicsTK is available under the Apache-2.0 license. Its main topics are bioimage-informatics, computer-vision, digital-slide-archive, histology.

❓ Frequently asked questions

What is HistomicsTK?

A Python toolkit for pathology image analysis algorithms.

Is HistomicsTK open source?

HistomicsTK is an open-source project. It is released under the Apache-2.0 license.

Is HistomicsTK free?

Yes. HistomicsTK is free and open source — you can use, modify and self-host it.

What license does HistomicsTK use?

HistomicsTK is available under the Apache-2.0 license.

What language is HistomicsTK written in?

HistomicsTK is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — HistomicsTK

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=digitalslidearchive-histomicstk)](https://olud.ai/project/digitalslidearchive-histomicstk.html)
More badge options →
🧬 Shares DNA with

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.