Dcm-anon: DICOM anonymizer with a verbatim-cited GDPR/HIPAA compliance manifest
Dcm-anon is an open-source DICOM anonymizer designed to ensure compliance with GDPR and HIPAA regulations. It provides a machine-verifiable compliance manifest that details the de-identification process, making it suitable for research data sharing. The tool addresses legal traceability gaps in cross-hospital data preparation by implementing a comprehensive audit trail and independent output verification.
- ▪Dcm-anon implements the DICOM PS3.15 Basic Application Level Confidentiality Profile, ensuring UID consistency across files.
- ▪The tool emits a tamper-evident JSON and Markdown artifact that maps each action to specific regulatory clauses.
- ▪It includes features like an audit log, independent output verification, and the ability to handle nested PHI in sequence items.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Ces107/dcm-anon |
| Publication time | Mon, 18 May 2026 08:28:48 +0000 |
| Retrieval time | 2026-05-18T08:34:56.132Z |
| Last seen | 2026-05-18T08:34:56.132Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dPcRg24NVGm2 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
dcm-anon OSS DICOM anonymizer with a verbatim-cited, machine-verifiable compliance manifest — for research-data sharing under GDPR Art. 35 DPIA and HIPAA Safe Harbor. Why this exists GDPR Art. 35 makes a DPIA mandatory for large-scale processing of health data, and best practice — endorsed by the EDPS, the EDPB pseudonymisation guidelines (01/2025), and HHS OCR — is to de-identify at the source site before moving research data off-prem. Doing that defensibly means: A traceable mapping from each tag you removed to the specific clause that obliges you to remove it. An independent second opinion that the output does not still contain PHI. An audit trail that a regulator, IRB, or ethics committee can verify without your help. Most DICOM anonymizers do step 1 implicitly and skip steps 2 and 3.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.