STAMP

About STAMP

STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology An efficient, ready‑to‑use workflow from whole‑slide image to biomarker prediction. STAMP is an end‑to‑end, weakly‑supervised deep‑learning pipeline that helps discover and evaluate candidate image‑based biomarkers from gigapixel histopathology slides, no pixel‑level annotations required.

agents bioimage-analysis bioinformatics deep-learning digital-pathology heatmaps-visualization-tool histopathology mcp mcp-server pathology

Quick Facts

Stars	119
Forks	54
Language	Python
Category	MCP Server
License	MIT
Quality Score	62.200388779584/100
Open Issues	11
Last Updated	2026-05-22
Created	2023-09-22
Platforms	mcp, python
Est. Tokens	~232k

Compatible Skills

These tools work well together with STAMP for enhanced workflows:

llm_agents — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
Machine-Learning-Guide — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
LLM-VM — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
LLM-Tools — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
ClawBio — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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Frequently Asked Questions

What is STAMP?

STAMP is Solid Tumor Associative Modeling in Pathology. It is categorized as a MCP Server with 119 GitHub stars.

What programming language is STAMP written in?

STAMP is primarily written in Python. It covers topics such as agents, bioimage-analysis, bioinformatics.

How do I install or use STAMP?

You can find installation instructions and usage details in the STAMP GitHub repository at github.com/KatherLab/STAMP. The project has 119 stars and 54 forks, indicating an active community.

What license does STAMP use?

STAMP is released under the MIT license, making it free to use and modify according to the license terms.

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