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Starting updates to docs (#7116)
### Description This updates the docs with a minor fix to the bundle metadata example and adds diagrams to help explain MONAI, and bundles vs. MAPs. ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Eric Kerfoot <eric.kerfoot@kcl.ac.uk>
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docs/images/MONAI_arch.png

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docs/images/MONAI_bundle_cloud.png

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docs/images/MONAI_clouds.png

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docs/images/MONAI_map_cloud.png

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docs/source/bundle_intro.rst

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Bundle
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======
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MONAI Bundles are a specification and file structure based way of distributing trained MONAI models with associated
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metadata, code, documentation, and other resources. These are meant to make it easier for you to distribute your model
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in a format that explains what the model is for, how to use it, how to reproduce the science you've done with it, and
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use it in other applications such as Label and Deploy.
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.. toctree::
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:maxdepth: 1
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Detailed bundle examples and get started tutorial: https://github.com/Project-MONAI/tutorials/tree/main/bundle
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A collection of medical imaging models in the MONAI Bundle format: https://github.com/Project-MONAI/model-zoo
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Bundle vs. MAPs
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---------------
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Bundles differ from MONAI Application Packages (MAPs) in that they focus on description, code definition, application,
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usage. MAPs focus on deployment, containerisation, integration into existing clinical systems, and other application
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areas relating to putting models into use.
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.. image:: ../images/MONAI_clouds.png
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:alt: Bundle and MAP Concepts
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:align: center
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As a user, bundles are networks and "programs" you would use directly for training, inference, reproducing results,
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and other tasks. Bundles can be integrated into MONAI Label apps to perform segmentation tasks through user interfaces,
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or into MAPs for deployment. They can be integrated into other container environments but this isn't their focus. A
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bundle in general is a more lightweight concept with less infrastructure
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For all applications relating to containerisation, portability, and deployment, MAPs are what you're looking for. A MAP
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is the contained environment for running an inference application directly or within an orchestration system. A bundle
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alone doesn't have the structure suitable for this use, a MAP must be provided which uses a bundle as the inference object.
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MAPs are also meant for inference only unlike bundles which should include training scripts. DICOM access is emphasised in
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MAPs since they are meant for clinical deployment and so must interface with clinical databases.

docs/source/index.rst

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- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
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- providing researchers with an optimized and standardized way to create and evaluate deep learning models.
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.. image:: ../images/MONAI_arch.png
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:alt: MONAI Architecture
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:align: center
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Features
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--------
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docs/source/mb_specification.rst

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"dtype": "float32",
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"value_range": [0, 1],
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"is_patch_data": false,
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"channel_def": {0: "image"}
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"channel_def": {"0": "image"}
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}
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},
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"outputs":{
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"dtype": "float32",
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"value_range": [],
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"is_patch_data": false,
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"channel_def": {0: "background", 1: "spleen"}
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"channel_def": {"0": "background", "1": "spleen"}
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}
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}
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}

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