Pith Number
pith:N2QHI7YB
pith:2025:N2QHI7YBJ4FEZ2WGLU6VTR4ESQ
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Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
arxiv:2501.12016 v1 · 2025-01-21 · cs.CV · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{N2QHI7YBJ4FEZ2WGLU6VTR4ESQ}
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
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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 | 2026-07-05T10:03:30.656821Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6ea0747f014f0a4ceac65d3d59c7849430168c94abbb80f8b086b647bd365375
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/N2QHI7YBJ4FEZ2WGLU6VTR4ESQ \
| 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: 6ea0747f014f0a4ceac65d3d59c7849430168c94abbb80f8b086b647bd365375
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "6c1fc6f54cf8cd08e62e685d689b9ab314230f3e74548d9d8818502e1863ba7c",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2025-01-21T10:16:00Z",
"title_canon_sha256": "b8e05efc37a3dda76a232949b053443855546971146d689db2d9e43e0b3bd30f"
},
"schema_version": "1.0",
"source": {
"id": "2501.12016",
"kind": "arxiv",
"version": 1
}
}