🚧Test using RST for README

README.rst

@@ -0,0 +1,199 @@
+InnerEye-DeepLearning
+=====================
+
+|Build Status|
+
+InnerEye-DeepLearning (IE-DL) is a toolbox for easily training deep
+learning models on 3D medical images. Simple to run both locally and in
+the cloud with
+`AzureML <https://docs.microsoft.com/en-gb/azure/machine-learning/>`__,
+it allows users to train and run inference on the following:
+
+- Segmentation models.
+- Classification and regression models.
+- Any PyTorch Lightning model, via a `bring-your-own-model
+ setup <bring_your_own_model.md>`__.
+
+In addition, this toolbox supports:
+
+- Cross-validation using AzureML, where the models for individual folds
+ are trained in parallel. This is particularly important for the
+ long-running training jobs often seen with medical images.
+- Hyperparameter tuning using
+ `Hyperdrive <https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters>`__.
+- Building ensemble models.
+- Easy creation of new models via a configuration-based approach, and
+ inheritance from an existing architecture.
+
+Documentation
+-------------
+
+For all documentation, including setup guides and APIs, please refer to
+the `IE-DL Read the Docs
+site <https://innereye-deeplearning.readthedocs.io/#>`__.
+
+Quick Setup
+-----------
+
+This quick setup assumes you are using a machine running Ubuntu with
+Git, Git LFS, Conda and Python 3.7+ installed. Please refer to the
+`setup guide <environment.md>`__ for more detailed instructions on
+getting InnerEye set up with other operating systems and installing the
+above prerequisites.
+
+1. Clone the InnerEye-DeepLearning repo by running the following
+ command:
+
+ .. code:: shell
+
+ git clone --recursive https://github.com/microsoft/InnerEye-DeepLearning & cd InnerEye-DeepLearning
+
+2. Create and activate your conda environment:
+
+ .. code:: shell
+
+ conda env create --file environment.yml && conda activate InnerEye
+
+3. Verify that your installation was successful by running the
+ HelloWorld model (no GPU required):
+
+ .. code:: shell
+
+ python InnerEye/ML/runner.py --model=HelloWorld
+
+If the above runs with no errors: Congratulations! You have successfully
+built your first model using the InnerEye toolbox.
+
+If it fails, please check the `troubleshooting page on the
+Wiki <https://github.com/microsoft/InnerEye-DeepLearning/wiki/Issues-with-code-setup-and-the-HelloWorld-model>`__.
+
+Full InnerEye Deployment
+------------------------
+
+We offer a companion set of open-sourced tools that help to integrate
+trained CT segmentation models with clinical software systems:
+
+- The
+ `InnerEye-Gateway <https://github.com/microsoft/InnerEye-Gateway>`__
+ is a Windows service running in a DICOM network, that can route
+ anonymized DICOM images to an inference service.
+- The
+ `InnerEye-Inference <https://github.com/microsoft/InnerEye-Inference>`__
+ component offers a REST API that integrates with the
+ InnerEye-Gateway, to run inference on InnerEye-DeepLearning models.
+
+Details can be found `here <deploy_on_aml.md>`__.
+
+.. figure:: images/deployment.png
+ :alt: docs/deployment.png
+
+Benefits of InnerEye-DeepLearning
+---------------------------------
+
+In combiniation with the power of AzureML, InnerEye provides the
+following benefits:
+
+- **Traceability**: AzureML keeps a full record of all experiments that
+ were executed, including a snapshot of the code. Tags are added to
+ the experiments automatically, that can later help filter and find
+ old experiments.
+- **Transparency**: All team members have access to each other’s
+ experiments and results.
+- **Reproducibility**: Two model training runs using the same code and
+ data will result in exactly the same metrics. All sources of
+ randomness are controlled for.
+- **Cost reduction**: Using AzureML, all compute resources (virtual
+ machines, VMs) are requested at the time of starting the training job
+ and freed up at the end. Idle VMs will not incur costs. Azure low
+ priority nodes can be used to further reduce costs (up to 80%
+ cheaper).
+- **Scalability**: Large numbers of VMs can be requested easily to cope
+ with a burst in jobs.
+
+Despite the cloud focus, InnerEye is designed to be able to run locally
+too, which is important for model prototyping, debugging, and in cases
+where the cloud can’t be used. Therefore, if you already have GPU
+machines available, you will be able to utilize them with the InnerEye
+toolbox.
+
+Licensing
+---------
+
+`MIT
+License <https://github.com/microsoft/InnerEye-DeepLearning/blob/main/LICENSE>`__
+
+**You are responsible for the performance, the necessary testing, and if
+needed any regulatory clearance for any of the models produced by this
+toolbox.**
+
+Acknowledging usage of Project InnerEye OSS tools
+-------------------------------------------------
+
+When using Project InnerEye open-source software (OSS) tools, please
+acknowledge with the following wording:
+
+ This project used Microsoft Research’s Project InnerEye open-source
+ software tools (https://aka.ms/InnerEyeOSS).
+
+Contact
+-------
+
+If you have any feature requests, or find issues in the code, please
+create an `issue on
+GitHub <https://github.com/microsoft/InnerEye-DeepLearning/issues>`__.
+
+Please send an email to [email protected] if you would like
+further information about this project.
+
+Publications
+------------
+
+Oktay O., Nanavati J., Schwaighofer A., Carter D., Bristow M., Tanno R.,
+Jena R., Barnett G., Noble D., Rimmer Y., Glocker B., O’Hara K., Bishop
+C., Alvarez-Valle J., Nori A.: Evaluation of Deep Learning to Augment
+Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA
+Netw Open. 2020;3(11):e2027426.
+`doi:10.1001/jamanetworkopen.2020.27426 <https://pubmed.ncbi.nlm.nih.gov/33252691/>`__
+
+Bannur S., Oktay O., Bernhardt M, Schwaighofer A., Jena R., Nushi B.,
+Wadhwani S., Nori A., Natarajan K., Ashraf S., Alvarez-Valle J., Castro
+D. C.: Hierarchical Analysis of Visual COVID-19 Features from Chest
+Radiographs. ICML 2021 Workshop on Interpretable Machine Learning in
+Healthcare. https://arxiv.org/abs/2107.06618
+
+Bernhardt M., Castro D. C., Tanno R., Schwaighofer A., Tezcan K. C.,
+Monteiro M., Bannur S., Lungren M., Nori S., Glocker B., Alvarez-Valle
+J., Oktay. O: Active label cleaning for improved dataset quality under
+resource constraints.
+https://www.nature.com/articles/s41467-022-28818-3. Accompagnying code
+`InnerEye-DataQuality <https://github.com/microsoft/InnerEye-DeepLearning/blob/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality/README.md>`__
+
+Contributing
+------------
+
+This project welcomes contributions and suggestions. Most contributions
+require you to agree to a Contributor License Agreement (CLA) declaring
+that you have the right to, and actually do, grant us the rights to use
+your contribution. For details, visit
+https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine
+whether you need to provide a CLA and decorate the PR appropriately
+(e.g., status check, comment). Simply follow the instructions provided
+by the bot. You will only need to do this once across all repos using
+our CLA.
+
+This project has adopted the `Microsoft Open Source Code of
+Conduct <https://opensource.microsoft.com/codeofconduct/>`__. For more
+information see the `Code of Conduct
+FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`__ or contact
+[email protected] with any additional questions or comments.
+
+Maintenance
+-----------
+
+This toolbox is maintained by the `Microsoft Medical Image Analysis
+team <https://www.microsoft.com/en-us/research/project/medical-image-analysis/>`__.
+
+.. |Build Status| image:: https://innereye.visualstudio.com/InnerEye/_apis/build/status/InnerEye-DeepLearning/InnerEye-DeepLearning-PR?branchName=main
+ :target: https://innereye.visualstudio.com/InnerEye/_build?definitionId=112&branchName=main

docs/source/README.rst

@@ -0,0 +1,199 @@
+InnerEye-DeepLearning
+=====================
+
+|Build Status|
+
+InnerEye-DeepLearning (IE-DL) is a toolbox for easily training deep
+learning models on 3D medical images. Simple to run both locally and in
+the cloud with
+`AzureML <https://docs.microsoft.com/en-gb/azure/machine-learning/>`__,
+it allows users to train and run inference on the following:
+
+- Segmentation models.
+- Classification and regression models.
+- Any PyTorch Lightning model, via a `bring-your-own-model
+ setup <bring_your_own_model.md>`__.
+
+In addition, this toolbox supports:
+
+- Cross-validation using AzureML, where the models for individual folds
+ are trained in parallel. This is particularly important for the
+ long-running training jobs often seen with medical images.
+- Hyperparameter tuning using
+ `Hyperdrive <https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters>`__.
+- Building ensemble models.
+- Easy creation of new models via a configuration-based approach, and
+ inheritance from an existing architecture.
+
+Documentation
+-------------
+
+For all documentation, including setup guides and APIs, please refer to
+the `IE-DL Read the Docs
+site <https://innereye-deeplearning.readthedocs.io/#>`__.
+
+Quick Setup
+-----------
+
+This quick setup assumes you are using a machine running Ubuntu with
+Git, Git LFS, Conda and Python 3.7+ installed. Please refer to the
+`setup guide <environment.md>`__ for more detailed instructions on
+getting InnerEye set up with other operating systems and installing the
+above prerequisites.
+
+1. Clone the InnerEye-DeepLearning repo by running the following
+ command:
+
+ .. code:: shell
+
+ git clone --recursive https://github.com/microsoft/InnerEye-DeepLearning & cd InnerEye-DeepLearning
+
+2. Create and activate your conda environment:
+
+ .. code:: shell
+
+ conda env create --file environment.yml && conda activate InnerEye
+
+3. Verify that your installation was successful by running the
+ HelloWorld model (no GPU required):
+
+ .. code:: shell
+
+ python InnerEye/ML/runner.py --model=HelloWorld
+
+If the above runs with no errors: Congratulations! You have successfully
+built your first model using the InnerEye toolbox.
+
+If it fails, please check the `troubleshooting page on the
+Wiki <https://github.com/microsoft/InnerEye-DeepLearning/wiki/Issues-with-code-setup-and-the-HelloWorld-model>`__.
+
+Full InnerEye Deployment
+------------------------
+
+We offer a companion set of open-sourced tools that help to integrate
+trained CT segmentation models with clinical software systems:
+
+- The
+ `InnerEye-Gateway <https://github.com/microsoft/InnerEye-Gateway>`__
+ is a Windows service running in a DICOM network, that can route
+ anonymized DICOM images to an inference service.
+- The
+ `InnerEye-Inference <https://github.com/microsoft/InnerEye-Inference>`__
+ component offers a REST API that integrates with the
+ InnerEye-Gateway, to run inference on InnerEye-DeepLearning models.
+
+Details can be found `here <deploy_on_aml.md>`__.
+
+.. figure:: images/deployment.png
+ :alt: docs/deployment.png
+
+Benefits of InnerEye-DeepLearning
+---------------------------------
+
+In combiniation with the power of AzureML, InnerEye provides the
+following benefits:
+
+- **Traceability**: AzureML keeps a full record of all experiments that
+ were executed, including a snapshot of the code. Tags are added to
+ the experiments automatically, that can later help filter and find
+ old experiments.
+- **Transparency**: All team members have access to each other’s
+ experiments and results.
+- **Reproducibility**: Two model training runs using the same code and
+ data will result in exactly the same metrics. All sources of
+ randomness are controlled for.
+- **Cost reduction**: Using AzureML, all compute resources (virtual
+ machines, VMs) are requested at the time of starting the training job
+ and freed up at the end. Idle VMs will not incur costs. Azure low
+ priority nodes can be used to further reduce costs (up to 80%
+ cheaper).
+- **Scalability**: Large numbers of VMs can be requested easily to cope
+ with a burst in jobs.
+
+Despite the cloud focus, InnerEye is designed to be able to run locally
+too, which is important for model prototyping, debugging, and in cases
+where the cloud can’t be used. Therefore, if you already have GPU
+machines available, you will be able to utilize them with the InnerEye
+toolbox.
+
+Licensing
+---------
+
+`MIT
+License <https://github.com/microsoft/InnerEye-DeepLearning/blob/main/LICENSE>`__
+
+**You are responsible for the performance, the necessary testing, and if
+needed any regulatory clearance for any of the models produced by this
+toolbox.**
+
+Acknowledging usage of Project InnerEye OSS tools
+-------------------------------------------------
+
+When using Project InnerEye open-source software (OSS) tools, please
+acknowledge with the following wording:
+
+ This project used Microsoft Research’s Project InnerEye open-source
+ software tools (https://aka.ms/InnerEyeOSS).
+
+Contact
+-------
+
+If you have any feature requests, or find issues in the code, please
+create an `issue on
+GitHub <https://github.com/microsoft/InnerEye-DeepLearning/issues>`__.
+
+Please send an email to [email protected] if you would like
+further information about this project.
+
+Publications
+------------
+
+Oktay O., Nanavati J., Schwaighofer A., Carter D., Bristow M., Tanno R.,
+Jena R., Barnett G., Noble D., Rimmer Y., Glocker B., O’Hara K., Bishop
+C., Alvarez-Valle J., Nori A.: Evaluation of Deep Learning to Augment
+Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA
+Netw Open. 2020;3(11):e2027426.
+`doi:10.1001/jamanetworkopen.2020.27426 <https://pubmed.ncbi.nlm.nih.gov/33252691/>`__
+
+Bannur S., Oktay O., Bernhardt M, Schwaighofer A., Jena R., Nushi B.,
+Wadhwani S., Nori A., Natarajan K., Ashraf S., Alvarez-Valle J., Castro
+D. C.: Hierarchical Analysis of Visual COVID-19 Features from Chest
+Radiographs. ICML 2021 Workshop on Interpretable Machine Learning in
+Healthcare. https://arxiv.org/abs/2107.06618
+
+Bernhardt M., Castro D. C., Tanno R., Schwaighofer A., Tezcan K. C.,
+Monteiro M., Bannur S., Lungren M., Nori S., Glocker B., Alvarez-Valle
+J., Oktay. O: Active label cleaning for improved dataset quality under
+resource constraints.
+https://www.nature.com/articles/s41467-022-28818-3. Accompagnying code
+`InnerEye-DataQuality <https://github.com/microsoft/InnerEye-DeepLearning/blob/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality/README.md>`__
+
+Contributing
+------------
+
+This project welcomes contributions and suggestions. Most contributions
+require you to agree to a Contributor License Agreement (CLA) declaring
+that you have the right to, and actually do, grant us the rights to use
+your contribution. For details, visit
+https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine
+whether you need to provide a CLA and decorate the PR appropriately
+(e.g., status check, comment). Simply follow the instructions provided
+by the bot. You will only need to do this once across all repos using
+our CLA.
+
+This project has adopted the `Microsoft Open Source Code of
+Conduct <https://opensource.microsoft.com/codeofconduct/>`__. For more
+information see the `Code of Conduct
+FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`__ or contact
+[email protected] with any additional questions or comments.
+
+Maintenance
+-----------
+
+This toolbox is maintained by the `Microsoft Medical Image Analysis
+team <https://www.microsoft.com/en-us/research/project/medical-image-analysis/>`__.
+
+.. |Build Status| image:: https://innereye.visualstudio.com/InnerEye/_apis/build/status/InnerEye-DeepLearning/InnerEye-DeepLearning-PR?branchName=main
+ :target: https://innereye.visualstudio.com/InnerEye/_build?definitionId=112&branchName=main