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Paper Citation Record · LEDGER

Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.14874.

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

pith.paper-citation-record.v1
2409.14874 v2

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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T15:44:32.345362Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:06:16.688164Z

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 21c43d68-fec5-49d5-a143-f1ce6bd7ba34 · inbound

In search of truth: Evaluating concordance of AI-based anatomy segmentation models cites this paper.

In search of truth: Evaluating concordance of AI-based anatomy segmentation models Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:08:32.980149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T21:04:17.071422Z digest=sha256:77dd9c1b53b9296fb3cf75eb78bc6c4e01349f6f386901a9537659ad7e5e9733

Observation 5fe6924c-7142-4495-bafe-fd609996d833 · inbound

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation cites this paper.

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:06:16.689538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T15:44:32.345362Z digest=sha256:1c315dea067ffb6500c0b92df4e6ae52892efbc5a783b78e1a1ebe29875a11df