# STAMP > Solid Tumor Associative Modeling in Pathology - **Type:** MCP server - **Install:** `agentstack add mcp-katherlab-stamp` - **Verified:** Pending review - **Seller:** [KatherLab](https://agentstack.voostack.com/s/katherlab) - **Installs:** 0 - **Category:** [Integrations](https://agentstack.voostack.com/c/integrations) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [KatherLab](https://github.com/KatherLab) - **Source:** https://github.com/KatherLab/STAMP ## Install ```sh agentstack add mcp-katherlab-stamp ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology [](https://www.nature.com/articles/s41596-024-01047-2) *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](#installation) or [walk through our Getting Started guide](getting-started.md) 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-extraction) (e.g., *Virchow‑v2*, *UNI‑v2*) and [slide level](getting-started.md#slide-level-encoding) (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*](https://www.nature.com/articles/s41596-024-01047-2) 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](https://www.sciencedirect.com/science/article/pii/S0893395225001425):** 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](https://www.researchsquare.com/article/rs-6443303/v1):** 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](https://arxiv.org/pdf/2408.15823):** 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](https://doi.org/10.1038/s41467-025-57283-x):** In an international early breast cancer study, STAMP performed slide tessellation and color normalization (e.g. 1.14 µm/px resolution, Macenko norm) as part of a multimodal transformer pipeline to predict recurrence risk (Oncotype DX scores) from pathology images. - **[Endometrial Cancer Subtyping](https://www.actscience.org/Portals/0/Translational%20Science%202025/Top%2050%20Posters/TS25_VincentWagner_OralPosterSession.pdf):** A recent endometrial cancer project employed a modified STAMP pipeline with a pre-trained vision transformer (Virchow2) to predict molecular tumor subtypes directly from H&E slides, achieving strong diagnostic performance in cross-validation. ## Installation To setup STAMP you need [uv](https://docs.astral.sh/uv/). > [!IMPORTANT] > We use the experimental `match runtime` feature of `uv` which was introduced in [version 0.8.5](https://github.com/astral-sh/uv/releases/tag/0.8.5). > Please empty your `triton` cache before installing STAMP: `rm -r ~/.triton`. ### Install or Update uv: ```bash # Install uv curl -LsSf https://astral.sh/uv/install.sh | sh # Update uv uv self update ``` ### Install STAMP from the Repository: ```bash git clone https://github.com/KatherLab/STAMP.git cd STAMP ``` ```bash # GPU (CUDA) Installation (excluding conchv1_5, gigapath and musk) uv sync --extra gpu source .venv/bin/activate ``` ```bash # CPU-only Installation (excluding conchv1_5, gigapath and musk) uv sync --extra cpu source .venv/bin/activate ``` For the full GPU stack (`conchv1_5`, `gigapath`, `musk`), install with the prebuilt flash-attn wheel — no compile required. Supported on Linux x86_64, Linux aarch64, or Windows x86_64, with Python 3.13, CUDA 13.0, and torch 2.10. Wheels are hosted on the [STAMP releases](https://github.com/KatherLab/STAMP/releases) page. ```bash # GPU (CUDA) Installation - prebuilt flash-attn wheel, no compile uv sync --extra gpu_prebuilt source .venv/bin/activate ``` If you encounter errors during installation please read Installation Troubleshooting [below](#installation-troubleshooting). If the prebuilt wheel does not fit your platform or you need a different flash-attn version, see [Advanced: Build flash-attn from source](#advanced-build-flash-attn-from-source). ### Additional Dependencies > [!IMPORTANT] > STAMP additionally requires OpenCV dependencies to be installed. > > For Ubuntu ```bash > apt update && apt install -y libgl1-mesa-glx > ``` > > For Ubuntu >= 23.10: > ```bash > apt update && apt install -y libgl1 libglx-mesa0 libglib2.0-0 > ``` ### Advanced: Build flash-attn from source > [!CAUTION] > Building flash-attn can take an extended amount of time and consume a lot of RAM and CPU time! > > You must have [Nvidia CUDA Toolkit 13.0](https://developer.nvidia.com/cuda-13-0-2-download-archive) installed and Nvidia Driver version 580 or newer. > > The `nvcc --version` command must indicate that 13.0 is installed and is currently in PATH: `Cuda compilation tools, release 13.0, V13.0.88`. > > If you get another version or `Command 'nvcc' not found`, add it to the PATH: > ```bash > export CUDA_HOME=/usr/local/cuda-13.0 > export PATH="${CUDA_HOME}/bin:$PATH" > ``` > > Run `nvcc --version` to ensure flash-attn will be built for CUDA 13.0. ```bash # GPU (CUDA) Installation - building flash-attn for supporting conchv1_5, gigapath and musk MAX_JOBS=2 uv sync --extra gpu_all # to speed up the build time increase max_jobs! This might use more RAM! source .venv/bin/activate ``` ## Basic Usage If the installation was successful, running `stamp` in your terminal should yield the following output: ``` $ stamp usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,preprocess,encode_slides,encode_patients,train,crossval,deploy,statistics,config,heatmaps} ... STAMP: Solid Tumor Associative Modeling in Pathology positional arguments: {init,preprocess,encode_slides,encode_patients,train,crossval,deploy,statistics,config,heatmaps} init Create a new STAMP configuration file at the path specified by --config preprocess Preprocess whole-slide images into feature vectors encode_slides Encode patch-level features into slide-level embeddings encode_patients Encode features into patient-level embeddings train Train a Vision Transformer model crossval Train a Vision Transformer model with cross validation for modeling.n_splits folds deploy Deploy a trained Vision Transformer model statistics Generate AUROCs and AUPRCs with 95%CI for a trained Vision Transformer model config Print the loaded configuration heatmaps Generate heatmaps for a trained model options: -h, --help show this help message and exit --config CONFIG_FILE_PATH, -c CONFIG_FILE_PATH Path to config file. Default: config.yaml ``` ## Getting Started Guide For a quick introduction how to run stamp, check out our [getting started guide](getting-started.md). ## Reference If you find our work useful in your research or if you use parts of this code please consider citing our [Nature Protocols publication](https://www.nature.com/articles/s41596-024-01047-2): ``` @Article{ElNahhas2024, author={El Nahhas, Omar S. M. and van Treeck, Marko and W{\"o}lflein, Georg and Unger, Michaela and Ligero, Marta and Lenz, Tim and Wagner, Sophia J. and Hewitt, Katherine J. and Khader, Firas and Foersch, Sebastian and Truhn, Daniel and Kather, Jakob Nikolas}, title={From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology}, journal={Nature Protocols}, year={2024}, month={Sep}, day={16}, issn={1750-2799}, doi={10.1038/s41596-024-01047-2}, url={https://doi.org/10.1038/s41596-024-01047-2} } ``` > [!NOTE] > This repo contains an updated version of the codebase. > For a version compatible with the instructions in the paper, > please check out [version 1 of STAMP][stamp v1]. [stamp paper]: https://www.nature.com/articles/s41596-024-01047-2 "From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology" [stamp v1]: https://github.com/KatherLab/STAMP/tree/v1 ## Installation Troubleshooting > [!NOTE] > Installing the GPU version of STAMP might force the compilation of the `flash-attn` package (as well as `mamba-ssm` and `causal_conv1d`). This can take a long time and requires a lot of memory. You can limit the number of parallel compilation jobs by setting the `MAX_JOBS` environment variable before running the installation command, e.g. `MAX_JOBS=4 uv sync --extra build --extra gpu`. #### Triton Errors If you encounter errors related to the [Triton package like the following](https://github.com/pytorch/pytorch/issues/153737): ```bash SystemError: PY_SSIZE_T_CLEAN macro must be defined for '#' formats ``` Try to delete the triton cache: ```bash rm -r ~/.triton ``` A re-installation might be necessary afterwards. #### Undefined Symbol Error If you encounter an error similar to the following when importing flash_attn, mamba or causal_conv1d on a GPU system, it usually indicates that the torch version in your environment does not match the torch version used to build the flash-attn, mamba or causal_conv1d package. This can happen if you already built these packages for another environment or if for any reason between the installation commands with only `--extra build` and `--extra gpu` the torch version was changed. ``` > import flash_attn_2_cuda as flash_attn_gpu E ImportError: [...]/.venv/lib/python3.12/site-packages/flash_attn_2_cuda.cpython-312-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE .venv/lib/python3.12/site-packages/flash_attn/flash_attn_interface.py:15: ImportError ``` In case you encounter this error on a gpu installation, you can fix it by going back to the environment just with `--extra build`, clearing the uv cache and then reinstalling the `--extra gpu` packages: ```bash uv cache clean flash_attn uv cache clean mamba-ssm uv cache clean causal_conv1d # Now it should re-build the packages with the correct torch version # With uv sync in the cloned repository uv sync --extra build uv sync --extra build --extra gpu ``` ## Reproducibility > [!NOTE] > We use a central `Seed` utility to set seeds for PyTorch, NumPy, and Python’s `random`. This makes data loading and model initialization reproducible. Always call `Seed.set(seed)` once at startup. > We do not enable [`torch.use_deterministic_algorithms()`](https://pytorch.org/docs/stable/notes/randomness.html#reproducibility) because it can cause large performance drops. Expect runs with the same seed to follow the same training trajectory, but not bit-for-bit identical low-level kernels. ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [KatherLab](https://github.com/KatherLab) - **Source:** [KatherLab/STAMP](https://github.com/KatherLab/STAMP) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: flagged — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-katherlab-stamp - Seller: https://agentstack.voostack.com/s/katherlab - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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