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Paper Citation Record · LEDGER

Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.09484.

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

pith.paper-citation-record.v1
2409.09484 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:17.116757Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:56:04.502152Z

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 e11aa6cd-0ed6-410e-9c79-f61587fc195e · inbound

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models cites this paper.

Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T14:09:17.116757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:17.116757Z digest=sha256:b4f8cb38e32bfb933e611140cf78808abade2651e5828616facfa5b5873f9327

Observation 0756f776-404d-4cdb-9da9-1c80605c9b28 · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:56:04.507057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:56:03.689653Z digest=sha256:a301479673001bdebc2e75220c71007ea212febc5cff662babc73f2805444208