by KatherLab · MCP Server · ★ 119
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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. Backed by a peer‑reviewed protocol and used in multi‑center studies across several tumor types, STAMP lets clinical researchers and machine‑learning engineers collaborate on reproducible computational‑pathology projects with a clear, structured workflow. Want to start now? Jump to Installation or walk through our Getting Started guide for a hands-on tutorial. Why choose STAMP? 🚀 Scalable: Run locally or on HPC (SLURM) with the same CLI; built to handle multi‑center cohorts and large WSI collections. 🎓 Beginner‑friendly & expert‑ready: Zero‑code CLI and YAML config for routine use; optional code‑level customization for advanced research. 🧩 Model‑rich: Out‑of‑the‑box support for +20 foundation models at [tile level](getting-started.md#feature-extrac
| 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 |
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STAMP is Solid Tumor Associative Modeling in Pathology. It is categorized as a MCP Server with 119 GitHub stars.
STAMP is primarily written in Python. It covers topics such as agents, bioimage-analysis, bioinformatics.
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.
STAMP is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to STAMP on Agent Skills Hub include biomcp, concierge, mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.