Pith Number
pith:6624UXM2
pith:2022:6624UXM2YRB2KIOXOHQNMYXRP7
not attested
not anchored
not stored
refs pending
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
arxiv:2206.07994 v1 · 2022-06-16 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{6624UXM2YRB2KIOXOHQNMYXRP7}
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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claim
4
Citations
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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 | 2026-07-05T04:32:21.049653Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f7b5ca5d9ac443a521d771e0d662f17fd54d6012cf94fbadc7528cce94ca063f
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6624UXM2YRB2KIOXOHQNMYXRP7 \
| 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: f7b5ca5d9ac443a521d771e0d662f17fd54d6012cf94fbadc7528cce94ca063f
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "d85f9eec17a7833b49c6b055049642f7ef3944886980728fc4e36e0a7ee2a413",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2022-06-16T08:19:33Z",
"title_canon_sha256": "3c50afac649f925c2ec3be5c072e5c9b2041d51a136f74120e15200323b4f93a"
},
"schema_version": "1.0",
"source": {
"id": "2206.07994",
"kind": "arxiv",
"version": 1
}
}