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Paper Citation Record · LEDGER

Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.24368.

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

pith.paper-citation-record.v1
2503.24368 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:42:09.895795Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T16:46:21.028570Z

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 0b54ab50-e684-4f35-8d09-9a907b5cc661 · inbound

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning cites this paper.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T18:42:09.895795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:42:09.895795Z digest=sha256:296d5aa0e44e1b7addc68a65074ac61d4a46441da211d46c6c2cd99ad1d76902

Observation 6e76368c-65dc-4dad-bc72-a06a9e993f13 · inbound

Federated Learning for Large Models in Medical Imaging: A Comprehensive Review cites this paper.

Federated Learning for Large Models in Medical Imaging: A Comprehensive Review Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

Reference 136

Resolution
unresolved
no resolver link, observed 2026-08-05T15:07:54.799937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:07:54.799937Z digest=sha256:63ff51dc860e24767252011f1137c15a90b041e350930f46d5673246b1068a87

Observation fb1369ec-0581-491a-9269-42ff8a3b6dad · inbound

An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation cites this paper.

An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-07-09T16:46:21.030289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-09T16:39:19.453073Z digest=sha256:7b08fc0cb0f37e5c908210d413b86b1427ec154fc7ebf45b8a3c5d86ac8d5231