Anticloud/anticloud-hi-ml

HI_ML

Governed Anticloud packaging of the upstream project HI_ML in category MEDICAL_HEALTH. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.

Upstream: HI_ML · Upstream pin: b67c1d27c6b17d8e8ff01f8c507f3cabdb307388 · Category: MEDICAL_HEALTH · Vendor: Anticloud FZ LLE · Licence: MIT

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What This Project Does

Microsoft Health Intelligence Machine Learning Toolbox

Overview

This toolbox aims at providing low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It helps to simplify and streamline work on deep learning models for healthcare and life sciences, by providing tested components (data loaders, pre-processing), deep learning models, and cloud integration tools.

This project consists of two Python packages, as well as project-specific codebases:

• PyPi package hi-ml-azure - providing helper functions for running in AzureML.
• PyPi package hi-ml - providing ML components.
• hi-ml-cpath: Models and workflows for working with histopathology images

Getting started

For the full toolbox (this will also install hi-ml-azure):

• Install from pypi via pip, by running pip install hi-ml

For just the AzureML helper functions:

• Install from pypi via pip, by running pip install hi-ml-azure

For the histopathology workflows, please follow the instructions here.

If you would like to contribute to the code, please check the developer guide.

Documentation

The detailed package documentation, with examples and API reference, is on readthedocs.

Quick start: Using the Azure layer

Use case: you have a Python script that does something - that could be training a model, or pre-processing some data. The hi-ml-azure package can help easily run that on Azure Machine Learning (AML) services.

Here is an example script that reads images from a folder, resizes and saves them to an output folder:

from pathlib import Path
if __name__ == '__main__':
    input_folder = Path("/tmp/my_dataset")
    output_folder = Path("/tmp/my_output")
    for file in input_folder.glob("*.jpg"):
        contents = read_image(file)
        resized = contents.resize(0.5)
        write_image(output_folder / file.name)

Doing that at scale can take a long time. We'd like to run that script in AzureML, consume the data from a folder in blob storage, and write the results back to blob storage.

With the hi-ml-azure package, you can turn that script into one that runs on the cloud by adding one function call:

from pathlib import Path
from health_azure import submit_to_azure_if_needed
if __name__ == '__main__':
    current_file = Path(__file__)
    run_info = submit_to_azure_if_needed(compute_cluster_name="preprocess-ds12",
                                         input_datasets=["images123"],
                                         # Omit this line if you don't create an output dataset (for example, in
                                         # model training scripts)
                                         output_datasets=["images123_resized"],
                                         default_datastore="my_datastore")
    # When running in AzureML, run_info.input_datasets and run_info.output_datasets will be populated,
    # and point to the data coming from blob storage. For runs outside AML, the paths will be None.
    # Replace the None with a meaningful path, so that we can still run the script easily outside AML.
    input_dataset = run_info.input_datasets[0] or Path("/tmp/my_dataset")
    output_dataset = run_info.output_datasets[0] or Path("/tmp/my_output")
    files_processed = []
    for file in input_dataset.glob("*.jpg"):
        contents = read_image(file)
        resized = contents.resize(0.5)
        write_image(output_dataset / file.name)
        files_processed.append(file.name)
    # Any other files that you would not consider an "output dataset", like metrics, etc, should be written to
    # a folder "./outputs". Any files written into that folder will later be visible in the AzureML UI.
    # run_info.output_folder already points to the correct folder.
    stats_file = run_info.output_folder / "processed_files.txt"
    stats_file.write_text("\n".join(files_processed))

Once these changes are in place, you can submit the script to AzureML by supplying an additional --azureml flag on the commandline, like python myscript.py --azureml.

That's it!

For details, please refer to the onboarding page.

For more examples, please see examples.md.

Issues

If you've found a bug in the code, please check the issues page. If no existing issue exists, please open a new one. Be sure to include

• A descriptive title
• Expected behaviour (including a code sample if possible)
• Actual behavior

Contributing

We welcome all contributions that help us achieve our aim of speeding up ML/AI research in health and life sciences. Examples of contributions are

• Data loaders for specific health & life sciences data
• Network architectures and components for deep learning models
• Tools to analyze and/or visualize data
• ...

Please check the detailed page about contributions.

Licensing

MIT 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.

Contact

If you have any feature requests, or find issues in the code, please create an issue on GitHub.

Contribution Licensing

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 projects using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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Installation

For the full toolbox (this will also install hi-ml-azure):

• Install from pypi via pip, by running pip install hi-ml

For just the AzureML helper functions:

• Install from pypi via pip, by running pip install hi-ml-azure

For the histopathology workflows, please follow the instructions here.

If you would like to contribute to the code, please check the developer guide.

Usage

See the upstream documentation quoted in What This Project Does above.

API

• PyPi package hi-ml - providing ML components.
• hi-ml-cpath: Models and workflows for working with histopathology images

Dependencies

Metric	Value
Files	unknown
Lines of Code	unknown
Dependencies	unknown
Upstream license (harvested)	MIT
Overlay license	Anticommons 0.1.0

Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.

Configuration

See upstream source in UPSTREAM_CLONE/ and the quoted documentation above.

Contributing

Contributing to this toolbox

We welcome all contributions that help us achieve our aim of speeding up Machine Learning (ML) / AI research in health and life sciences.

Examples of contributions are

• Data loaders for specific health & life sciences data
• Network architectures and components for deep learning models
• Tools to analyze and/or visualize data
• Bug reports
• Documentation improvements, including fixing typos
• Suggestions about codebase improvements

All contributions to the toolbox need to come with unit tests, and will be reviewed when a pull request is started. If in doubt, reach out to the core hi-ml team before starting your work.

Please look through the existing folder structure to find a good home for your contribution.

For a full set of design and guidelines, please check our coding guidelines documentation.

License

Upstream © its respective contributors under MIT (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.

Upstream

• project: HI_ML
• Pinned SHA: b67c1d27c6b17d8e8ff01f8c507f3cabdb307388
• source: UPSTREAM_CLONE/ (pinned at the SHA above)
• Upstream README source: UPSTREAM_CLONE/README.md

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is ffa62a3076381d854a94f8d41663843bc7f6868d2c891d4f4357faf3260b1e58.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.