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STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology

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This repository contains the accompanying code for the steps described in the Nature Protocols paper: "From Whole Slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology".

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.

Installing stamp

To install stamp, run:

# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"

Important

STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.

For Ubuntu < 23.10:

apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV

For Ubuntu >= 23.10:

apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV

If the installation was successful, running stamp in your terminal should yield the following output:

$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp

For a quick introduction how to run stamp, check out our getting started guide.

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:

@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}
}

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GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
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STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
BranchesTags
Repository files navigation
STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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, 'i');
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var __m = "*";
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GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
BranchesTags
Repository files navigation
STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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Solid Tumor Associative Modeling in Pathology
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
BranchesTags
Repository files navigation
STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
BranchesTags
Repository files navigation
STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
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STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

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GitHub - DEschweiler/STAMP: Solid Tumor Associative Modeling in Pathology · GitHub
Skip to content
BranchesTags
Repository files navigation
STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology
CI
This repository contains the accompanying code for the steps described in the Nature Protocols paper:
"From Whole Slide Image to Biomarker Prediction:
A Protocol for End-to-End Deep Learning in Computational Pathology".
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.
Installing stamp
To install stamp, run:
# We recommend using a virtual environment to install stamp
python -m venv .venv
. .venv/bin/activate
pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP"
Important
STAMP additionally requires OpenSlide to be installed, as well as OpenCV dependencies.
For Ubuntu < 23.10:
apt update && apt install -y openslide-tools libgl1-mesa-glx # libgl1-mesa-glx is needed for OpenCV
For Ubuntu >= 23.10:
apt update && apt install -y openslide-tools libgl1 libglx-mesa0 libglib2.0-0 # libgl1, libglx-mesa0, libglib2.0-0 are needed for OpenCV
If the installation was successful, running stamp in your terminal should yield the following output:
$ stamp
usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ...
STAMP: Solid Tumor Associative Modeling in Pathology
positional arguments:
{init,setup,preprocess,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
train Train a Vision Transformer model
crossval Train a Vision Transformer model with cross validation
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

Running stamp
For a quick introduction how to run stamp,
check out our getting started guide.
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:
@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}
}

About
Solid Tumor Associative Modeling in Pathology
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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