pith:YYUIGAWD
Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration
A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.
arxiv:2604.10245 v2 · 2026-04-11 · cs.CV · physics.med-ph
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Claims
Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.
The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset.
A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.
Receipt and verification
| First computed | 2026-05-21T01:05:18.811077Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT \
| 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: c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515
Canonical record JSON
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