Paper Citation Record · LEDGER

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2304.04155.

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

pith.paper-citation-record.v1
2304.04155 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:28:34.598403Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:57.654346Z

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 482b6920-681e-44c5-b3ee-8598c21396c2 · inbound

Data-Centric Foundation Models in Computational Healthcare: A Survey cites this paper.

Data-Centric Foundation Models in Computational Healthcare: A Survey Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:13:52.728288Z

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.

Observation 3b0748af-144c-4a3d-8889-be513e67cfa9 · inbound

SAM 2: Segment Anything in Images and Videos cites this paper.

SAM 2: Segment Anything in Images and Videos Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:56:25.386253Z

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.

Observation e65c1a70-d689-40cf-8bf2-951a8149cd6c · inbound

TAGS: 3D Tumor-Adaptive Guidance for SAM cites this paper.

TAGS: 3D Tumor-Adaptive Guidance for SAM Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:34.598403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faf1ee2e-8144-4dc3-8913-dbb2dfa58935 · inbound

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment cites this paper.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:45.610444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1fe0059-9682-4bc7-8e59-6e641b8b28f3 · inbound

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey cites this paper.

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 176

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:25.551918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 148c37b2-db02-4efb-9690-439707f7a6eb · inbound

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations cites this paper.

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:17:57.655668Z

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.