Paper Citation Record · LEDGER

SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.13759.

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

pith.paper-citation-record.v1
2308.13759 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:49:34.302867Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T17:26:01.464152Z

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 093ac046-cb7b-4ef4-b7fd-d81995f1c056 · inbound

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation cites this paper.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 28

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T17:26:01.469244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e517b180-040a-47a5-8011-9e9c8e63a191 · inbound

Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation cites this paper.

Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:49:34.302867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 386945aa-b216-4f34-916e-35b1cde41682 · inbound

Adapting a Segmentation Foundation Model for Medical Image Classification cites this paper.

Adapting a Segmentation Foundation Model for Medical Image Classification SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:48:33.738376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c943fce-2147-450a-bd06-a9fc2ff259da · inbound

Recent Advances in Medical Imaging Segmentation: A Survey cites this paper.

Recent Advances in Medical Imaging Segmentation: A Survey SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 122

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.776596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.