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 (e.g., Virchow‑v2, UNI‑v2) and slide level (e.g., TITAN, COBRA).
• 🔬 Weakly‑supervised: End‑to‑end MIL with Transformer aggregation for training, cross‑validation and external deployment; no pixel‑level labels required.
• 🧮 Multi-task learning: Unified framework for classification, multi-target classification, regression, and cox-based survival analysis.
• 📊 Stats & results: Built‑in metrics and patient‑level predictions, ready for analysis and reporting.
• 🖼️ Explainable: Generates heatmaps and top‑tile exports out‑of‑the‑box for transparent model auditing and publication‑ready figures.
• 🤝 Collaborative by design: Clinicians drive hypothesis & interpretation while engineers handle compute; STAMP’s modular CLI mirrors real‑world workflows and tracks every step for full reproducibility.
• 📑 Peer‑reviewed: Protocol published in Nature Protocols and validated across multiple tumor types and centers.
• 🔗 MCP Support: Compatible with Model Context Protocol (MCP) via the `mcp/` module, ready for integration into next-gen agentic AI workflows.

Real-World Examples of STAMP in Action

• Squamous Tumors & Survival: In a multi-cohort study spanning four squamous carcinoma types (head & neck, esophageal, lung, cervical), STAMP was used to extract slide-level features for a deep learning model that predicted patient survival directly from H&E whole-slide images.

• Inflammatory Bowel Disease Atlas: In a 1,002-patient multi-center IBD study, all histology slides were processed with the STAMP workflow, enabling a weakly-supervised MIL model to accurately predict histologic disease activity scores from H&E tissue sections.

• Foundation Model Benchmarking: A large-scale evaluation of 19 pathology foundation models built its pipeline on STAMP (v1.1.0) for standardized WSI tiling and feature extraction, demonstrating STAMP’s utility as an open-source framework for reproducible model training across diverse cancer biomarkers.

• Breast Cancer Risk Stratification: In an international early breast cancer study, STAMP performed slide tessellation and color normalizati

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