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Paper Citation Record · LEDGER

RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.08152.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.08152 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:56:42.562244Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T23:15:12.808920Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation df69865f-0716-4127-bf45-fe2ce2bfe206 · inbound

Rapid patient-specific neural networks for intraoperative X-ray to volume registration cites this paper.

Rapid patient-specific neural networks for intraoperative X-ray to volume registration RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-22T23:15:12.812215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-22T23:14:26.488603Z digest=sha256:3b9bd44069f8c634df7eec30e296877c9163b1d7c985cb761c8ca339db147fda

Observation 9b4206c8-4062-4de5-8f85-064678db5f63 · inbound

Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones cites this paper.

Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-01T04:56:42.562244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T04:56:42.562244Z digest=sha256:bea2b07bf13fe190ba5a74d62918def00f783c11b2701c578ece866274c1f092