Dicomsweep

pip install cognis-dicomsweep

dicomsweep scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ dicomsweep-emit --version
dicomsweep 0.1.0

$ dicomsweep-emit --help
usage: dicomsweep [-h] [--version] [--format {table,json}] <command> ...

De-identify DICOM tag metadata per a research-safe profile.

positional arguments:
  <command>
    scan                detect PHI tags (read-only; exits 1 if any are found)
    sweep               write a de-identified copy of the file

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

examples:
  dicomsweep scan scan.dcm
  dicomsweep scan scan.dcm --format json | jq .
  dicomsweep sweep scan.dcm -o scan.safe.dcm

Blocks above are real dicomsweep output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"Findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Traffic",
        "description": "Potential malicious activity detected on port 443.",
        "created_at": "2023-02-15T14:30:00Z",
        "updated_at": "2023-02-15T14:30:00Z",
        "labels": ["Network", "Malware"],
        "threats": [
            {
                "id": "ABC123",
                "name": "Malware XYZ"
            }
        ]
    },
    {
        "id": "2345678901",
        "title": "Unusual File Access",
        "description": "User accessed a file with suspicious permissions.",
        "created_at": "2023-02-16T10:15:00Z",
        "updated_at": "2023-02-16T10:15:00Z",
        "labels": ["File", "Anomaly"],
        "threats": [
            {
                "id": "DEF456",
                "name": "Ransomware ABC"
            }
        ]
    }
]
}

Usage — step by step

1. Install the CLI:

   pip install dicomsweep
   

2. Scan a DICOM file for PHI tags (read-only; exits 1 if any are found):

   dicomsweep scan study.dcm
   

3. Sweep the file to write a de-identified copy (defaults to <name>.safe.dcm):

   dicomsweep sweep study.dcm --output study.safe.dcm
   

4. Read the output. The global --format json flag emits a machine-readable report of detected/removed tags:

   dicomsweep --format json scan study.dcm > phi.json
   

5. Wire it into a pipeline — gate on the scan exit code, then sweep before export:

   dicomsweep scan study.dcm && echo clean || dicomsweep sweep study.dcm
   

Contents

• Why dicomsweep? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why dicomsweep?

One command turns a folder of scans into a research-safe dataset, including OCR-based pixel-burn removal that most free tools skip — irresistible for AI-imaging researchers.

dicomsweep is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Tag Name

• ✅ Parse Dicom

• ✅ Scan Dataset

• ✅ Scan File

• ✅ Sweep Dataset

• ✅ Sweep File

• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer

• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-dicomsweep

dicomsweep --version

dicomsweep scan .                       # scan current project

dicomsweep scan . --format json         # machine-readable

dicomsweep scan . --fail-on high        # CI gate (non-zero exit)

Example

$ dicomsweep scan .

  [HIGH    ] DIC-001  example finding             (./src/app.py)

  [MEDIUM  ] DIC-002  another signal              (./config.yaml)



  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[dicomsweep<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

dicomsweep is interoperable with every popular way of using AI:

• MCP server — dicomsweep mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)

• OpenAI-compatible / JSON — pipe dicomsweep scan . --format json into any agent or LLM

• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line

• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

| | Cognis dicomsweep | pydicom |

|---|:---:|:---:|

| Self-hostable, no account | ✅ | varies |

| Single command, zero config | ✅ | ⚠️ |

| JSON + SARIF for CI | ✅ | varies |

| MCP-native (AI agents) | ✅ | ❌ |

| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |

| Open license | ✅ COCL | varies |

*Built in the spirit of pydicom / DICOM Cleaner (RSNA), re-framed the Cognis way. Missing a credit? Open a PR.*

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (dicomsweep mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/dicomsweep.git"    # pip (works today)

pipx install "git+https://github.com/cognis-digital/dicomsweep.git"   # isolated CLI

uv tool install "git+https://github.com/cognis-digital/dicomsweep.git" # uv

pip install cognis-dicomsweep                                          # PyPI (when published)

docker run --rm ghcr.io/cognis-digital/dicomsweep:latest --help        # Docker

brew install cognis-digital/tap/dicomsweep                             # Homebrew tap

curl -fsSL https://raw.githubusercontent.com/cognis-digital/dicomsweep/main/install.sh | sh

| Linux | macOS | Windows | Docker | Cloud |

|---|---|---|---|---|

| scripts/setup-linux.sh | scripts/setup-macos.sh | scripts/setup-windows.ps1 | docker run ghcr.io/cognis-digital/dicomsweep | DEPLOY.md (AWS/Azure/GCP/k8s) |

Related Cognis tools

• phiscrub — Stream-scan logs, CSVs, and free-text notes for PHI (names, MRNs, SSNs, dates, addresses) and redact or tokenize in place.

• fhirlint — Validate FHIR R4/R5 resources and bundles against profiles (US Core, etc.) with precise, line-level error reporting.

• hl7tap — Parse, pretty-print, diff, and replay HL7 v2 messages over MLLP from the terminal.

• consentledger — Maintain a tamper-evident, hash-chained audit log of patient-data access and consent events.

• synthcohort — Generate statistically realistic synthetic patient cohorts (FHIR/CSV) from a schema spec for dev and testing.

• trialwatch — Query, diff, and monitor ClinicalTrials.gov records, alerting on status, enrollment, or result changes.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If dicomsweep saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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