Pith Number
pith:SM5PA7HP
pith:2017:SM5PA7HPVY7J3543ZD55OIVCJE
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Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions
arxiv:1709.01602 v1 · 2017-09-05 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{SM5PA7HPVY7J3543ZD55OIVCJE}
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
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4
Citations
5
Replications
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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 | 2026-05-18T00:35:54.782045Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
933af07cefae3e9df79bc8fbd722a2490f0eea53a24deabbf1e8dbe27a12e60b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SM5PA7HPVY7J3543ZD55OIVCJE \
| 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: 933af07cefae3e9df79bc8fbd722a2490f0eea53a24deabbf1e8dbe27a12e60b
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "da3eae1d5f6612b1e8f914fb349a0d988645b51b178298d805853b981ce16fc7",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2017-09-05T21:41:58Z",
"title_canon_sha256": "ed2f11c281e0e3aa7cd3dfa9ea3928ad0ae04d43d70bb63ad898427028af2967"
},
"schema_version": "1.0",
"source": {
"id": "1709.01602",
"kind": "arxiv",
"version": 1
}
}