STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology

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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 (e.g., Virchow‑v2, UNI‑v2) and slide level (e.g., TITAN, ).