🤖🔬 PathML: Tools for computational pathology

⭐ PathML objective is to lower the barrier to entry to digital pathology

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases.

🚀 The fastest way to get started?

docker pull pathml/pathml && docker run -it -p 8888:8888 pathml/pathml

Done, what analyses can I write now? 👉

This AI will:

🤖 write digital pathology analyses for you
🔬 walk you through the code, step-by-step
🎓 be your teacher, as you embark on your digital pathology journey

More information here and usage examples here

📖 Official PathML Documentation

View the official PathML Documentation on readthedocs

🔥 Examples! Examples! Examples!

↴ Jump to the gallery of examples below

1. Installation

PathML is an advanced tool for pathology image analysis. Below are simplified instructions to help you install PathML on your system. Whether you’re a user or a developer, follow these steps to get started.

1.1 Prerequisites

We recommend using Micromamba for managing your environments. We provide instructions on how to install PathML via Micromamba below. In addition, we also provide instructions on how to install via Miniconda should you have a license.

Installation

If you don’t have Miniconda installed, you can download Miniconda here.

Upating Micromamba

Make sure you have the recent version of Micromamba by using the following command:

micromamba update

Updating Conda and Using libmamba (Optional)

If you are using Micromamba, you can skip to the next section.

We recommend that Anaconda/Microconda users complete the following steps to update your Conda version and use libmamba to resolve dependency conflicts.

Recent versions of Conda have integrated libmamba, a faster dependency solver. To benefit from this improvement, first ensure your Conda is updated:

conda update -n base conda

Then, to install and set the new libmamba solver, run:

conda install -n base conda-libmamba-solver
conda config --set solver libmamba

Note: these instructions are for Linux. Commands may be different for other platforms.

Platform-Specific External Dependencies

For installation methods 1) and 2), you will need to install the following platform-specific packages.

Linux: Install external dependencies with Apt:

sudo apt-get install openslide-tools g++ gcc libblas-dev liblapack-dev

MacOS: Install external dependencies with Brew:
```
brew install openslide
```
Windows:

Option A: Install with vcpkg:
```
vcpkg install openslide
```
Option B: Using Pre-built OpenSlide Binaries (Alternative) For Windows users, an alternative to using vcpkg is to download and use pre-built OpenSlide binaries. This method is recommended if you prefer a quicker setup.

Download the OpenSlide Windows binaries from the OpenSlide Downloads page.
Extract the archive to your desired location, e.g., C:\OpenSlide\.

1.2 PathML Installation Methods

1.2.1 Install with Micromamba and pip (Recommended for Users)

Create and Activate Micromamba Environment and install openjdk

micromamba create -n pathml  'openjdk<=18.0' -c conda-forge python=3.9
micromamba activate pathml

Install `PathML` from PyPI

pip install pathml

1.2.2 Install with Anaconda and pip

Create and Activate Conda Environment

conda create --name pathml python=3.9
conda activate pathml

Install OpenJDK

conda install -c conda-forge 'openjdk<=18.0'

Install `PathML` from PyPI

pip install pathml

1.2.3 Install from Source (Recommended for Developers)

Clone repository

git clone https://github.com/Dana-Farber-AIOS/pathml.git
cd pathml

Create conda environment

Linux and Windows:

conda env create -f environment.yml
conda activate pathml

To use GPU acceleration for model training or other tasks, you must install CUDA. The default CUDA version in our environment file is 11.6. To install a different CUDA version, refer to the instructions here).

MacOS:

conda env create -f requirements/environment_mac.yml
conda activate pathml

Install `PathML` from source:

pip install -e .

1.2.4 Use Docker Container

First, download or build the PathML Docker container:

pathml-docker-installation

Option A: download PathML container from Docker Hub
```
 docker pull pathml/pathml:latest
```
Optionally specify a tag for a particular version, e.g. docker pull pathml/pathml:2.0.2. To view possible tags, please refer to the PathML DockerHub page.

Option B: build docker container from source

 git clone https://github.com/Dana-Farber-AIOS/pathml.git
 cd pathml
 docker build -t pathml/pathml .

Then connect to the container:

docker run -it -p 8888:8888 pathml/pathml

The above command runs the container, which is configured to spin up a jupyter lab session and expose it on port 8888. The terminal should display a URL to the jupyter lab session starting with http://127.0.0.1:8888/lab?token=<.....>. Navigate to that page and you should connect to the jupyter lab session running on the container with the pathml environment fully configured. If a password is requested, copy the string of characters following the token= in the url.

Note that the docker container requires extra configurations to use with GPU.
Note that these instructions assume that there are no other processes using port 8888.

Please refer to the Docker run documentation for further instructions on accessing the container, e.g. for mounting volumes to access files on a local machine from within the container.

1.2.5 Use Google Colab

To get PathML running in a Colab environment:

import os
!pip install openslide-python
!apt-get install openslide-tools
!apt-get install openjdk-17-jdk-headless -qq > /dev/null
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-17-openjdk-amd64"
!update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/bin/java
!java -version
!pip install pathml

Thanks to all of our open-source collaborators for helping maintain these installation instructions!
Please open an issue for any bugs or other problems during installation process.

1.3. Import PathML

After you have installed all necessary dependencies and PathML itself, import it using the following command:

import pathml

For Windows users, insert the following code snippet at the beginning of your Python script or Jupyter notebook before importing PathML. This code sets up the DLL directory for OpenSlide, ensuring that the library is properly loaded:

# The path can also be read from a config file, etc.
OPENSLIDE_PATH = r'c:\path\to\openslide-win64\bin'

import os
if hasattr(os, 'add_dll_directory'):
    # Windows-specific setup
    with os.add_dll_directory(OPENSLIDE_PATH):
        import openslide
else:
    # For other OSes, this step is not needed
    import openslide

# Now you can proceed with using PathML
import pathml

This code snippet ensures that the OpenSlide DLLs are correctly found by Python on Windows systems. Replace c:\path\to\openslide-win64\bin with the actual path where you extracted the OpenSlide binaries.

If you encounter any DLL load failures, verify that the OpenSlide bin directory is correctly added to your PATH.

1.4 CUDA

To use GPU acceleration for model training or other tasks, you must install CUDA. This guide should work, but for the most up-to-date instructions, refer to the official PyTorch installation instructions.

Check the version of CUDA:

nvidia-smi

Replace both instances of ‘cu116’ in requirements/requirements_torch.txt with the CUDA version you see. For example, for CUDA 11.7, ‘cu116’ becomes ‘cu117’.

Then create the environment:

conda env create -f environment.yml
conda activate pathml

After installing PyTorch, optionally verify successful PyTorch installation with CUDA support:

python -c "import torch; print(torch.cuda.is_available())"

2. Using with Jupyter (optional)

Jupyter notebooks are a convenient way to work interactively. To use PathML in Jupyter notebooks:

2.1 Set JAVA_HOME environment variable

PathML relies on Java to enable support for reading a wide range of file formats. Before using PathML in Jupyter, you may need to manually set the JAVA_HOME environment variable specifying the path to Java. To do so:

Get the path to Java by running echo $JAVA_HOME in the terminal in your pathml conda environment (outside of Jupyter)

Set that path as the JAVA_HOME environment variable in Jupyter:

 import os
 os.environ["JAVA_HOME"] = "/opt/conda/envs/pathml" # change path as needed

2.2 Register environment as an IPython kernel

conda activate pathml
conda install ipykernel
python -m ipykernel install --user --name=pathml

This makes the pathml environment available as a kernel in jupyter lab or notebook.

3. Examples

Now that you are all set with PathML installation, let’s get started with some analyses you can easily replicate:

4. Citing & known uses

If you use PathML please cite:

J. Rosenthal et al., “Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology.” Molecular Cancer Research, 2022.

So far, PathML was referenced in 20+ manuscripts:

5. Users

This is where in the world our most enthusiastic supporters are located:

and this is where they work:

Source: https://ossinsight.io/analyze/Dana-Farber-AIOS/pathml#people

6. Contributing

PathML is an open source project. Consider contributing to benefit the entire community!

There are many ways to contribute to PathML, including:

Submitting bug reports
Submitting feature requests
Writing documentation and examples
Fixing bugs
Writing code for new features
Sharing workflows
Sharing trained model parameters
Sharing PathML with colleagues, students, etc.

See contributing for more details.

7. License

The GNU GPL v2 version of PathML is made available via Open Source licensing. The user is free to use, modify, and distribute under the terms of the GNU General Public License version 2.

Commercial license options are available also.

8. Contact

Questions? Comments? Suggestions? Get in touch!

pathml@dfci.harvard.edu

1. Installation

1.1 Prerequisites

Installation

Upating Micromamba

Updating Conda and Using libmamba (Optional)

Platform-Specific External Dependencies

1.2 PathML Installation Methods

1.2.1 Install with Micromamba and pip (Recommended for Users)

Create and Activate Micromamba Environment and install openjdk

Install PathML from PyPI

1.2.2 Install with Anaconda and pip

Create and Activate Conda Environment

Install OpenJDK

Install PathML from PyPI

1.2.3 Install from Source (Recommended for Developers)

Clone repository

Create conda environment

Install PathML from source:

1.2.4 Use Docker Container

1.2.5 Use Google Colab

1.3. Import PathML

1.4 CUDA

2. Using with Jupyter (optional)

2.1 Set JAVA_HOME environment variable

2.2 Register environment as an IPython kernel

3. Examples

4. Citing & known uses

5. Users

6. Contributing

7. License

8. Contact

Install `PathML` from PyPI

Install `PathML` from PyPI

Install `PathML` from source: