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        "author": "Vanessa Sochat",
        "author_email": "vsochat@stanford.edu",
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            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering",
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        "description": "# Deidentify (deid)\n\nBest effort anonymization for medical images in Python.\n\n[![DOI](https://zenodo.org/badge/94163984.svg)](https://zenodo.org/badge/latestdoi/94163984)\n[![Build Status](https://travis-ci.org/pydicom/deid.svg?branch=master)](https://travis-ci.org/pydicom/deid)\n\nPlease see our [Documentation](https://pydicom.github.io/deid/)\n\nThese are basic Python based tools for working with medical images and text, specifically for de-identification.\nThe cleaning method used here mirrors the one by CTP in that we can identify images based on known\nlocations. We are looking for collaborators to develop and validate an OCR cleaning method! Please reach out if you would like to help work on this.\n\n\n## Installation\n\n### Local\nFor the stable release, install via pip:\n\n```bash\npip install deid\n```\n\nFor the development version, install from Github:\n\n```bash\npip install git+git://github.com/pydicom/deid\n```\n\n### Docker\n\n```bash\ndocker build -t pydicom/deid .\ndocker run pydicom/deid --help\n```\n\n## Issues\nIf you have an issue, or want to request a feature, please do so on our [issues board](https://www.github.com/pydicom/deid/issues)",
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