Pith. sign in

Paper Citation Record · LEDGER

UniverSeg: Universal Medical Image Segmentation

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.06131.

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

pith.paper-citation-record.v1
2304.06131 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:08:47.752179Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:56:01.969865Z

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 b382b340-0762-4e65-ae18-a89a1f71a9f4 · inbound

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain cites this paper.

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain UniverSeg: Universal Medical Image Segmentation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T13:37:02.760984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:37:02.760984Z digest=sha256:b8766a6dd18e74820700b9a117c6f64479ae894885f3601835946cdb21392c56

Observation 16eb387f-fcb9-41f8-9bd6-04167387ddc4 · inbound

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research cites this paper.

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research UniverSeg: Universal Medical Image Segmentation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T20:08:47.752179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:08:47.752179Z digest=sha256:cdd76435d7356278e594b3e1673ec7c2a468886791f410744b8ff60f4e7c32d2

Observation 056e1f24-e60f-4c0d-9db8-a0ca664ee8f4 · inbound

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting cites this paper.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting UniverSeg: Universal Medical Image Segmentation

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T18:32:46.875897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:32:46.875897Z digest=sha256:1eeaebffc605b9771ce29f0c1b5e4b9861da0e61a7eb138ddb14f8e9dd4a15ee

Observation 51df0f2d-df56-4134-ba66-4b7094172f73 · inbound

Is Visual in-Context Learning for Compositional Medical Tasks within Reach? cites this paper.

Is Visual in-Context Learning for Compositional Medical Tasks within Reach? UniverSeg: Universal Medical Image Segmentation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T21:12:05.786244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:12:05.786244Z digest=sha256:80f87c3a61a3aebf23d4218c2dd03c9534b971047a389f10703ce6236d6dca81

Observation 667f49f3-a586-4c76-80cd-d53b7f7a32b6 · inbound

Scaling In-Context Segmentation with Hierarchical Supervision cites this paper.

Scaling In-Context Segmentation with Hierarchical Supervision UniverSeg: Universal Medical Image Segmentation

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:56:01.976355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T15:16:49.996084Z digest=sha256:8e0392222f80b14794542bb485b3eacf91502e0d3c25a8f87475a9ce9974fb08