Pith Number
pith:L5LNWVNQ
pith:2026:L5LNWVNQSSWOHCOREJEPTN3DYK
not attested
not anchored
not stored
refs pending
Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models
arxiv:2606.18749 v1 · 2026-06-17 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{L5LNWVNQSSWOHCOREJEPTN3DYK}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-06-19T16:11:46.439178Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5f56db55b094ace389d12248f9b763c29b89bbe131abcc5f9a40f1f8c13391d4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/L5LNWVNQSSWOHCOREJEPTN3DYK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5f56db55b094ace389d12248f9b763c29b89bbe131abcc5f9a40f1f8c13391d4
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "4590588b4d62bedbb6dcabbff4688285143bb6c6403872896e3a6740a238e6ea",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2026-06-17T06:52:07Z",
"title_canon_sha256": "4550a3d88510d5ccc3135d5dcf08fc4b8e0be7f391fbb3ee35b493d657935bc6"
},
"schema_version": "1.0",
"source": {
"id": "2606.18749",
"kind": "arxiv",
"version": 1
}
}