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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:34:08.909151Z

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T04:13:05.328492Z digest=sha256:4b88b9260c1927262e39b8ddb7d5f544019ec67401ce6ee3a95891e736e0301d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T13:56:25.331304Z digest=sha256:f608fba5044192b3c28b44442188ccaf8bbb4cdedc563bc0f7fed323077cf124

Observation eb9c6d21-b55d-4700-b6d7-4d291f6b4222 · inbound

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription cites this paper.

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-09T11:34:08.909151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:34:08.909151Z digest=sha256:fb98ca48c92069d948262157d6db887d53b1447f066a7babf0d594232bba7372

Observation f06b2865-536d-434d-84bd-0dadfa6dd7ec · inbound

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level cites this paper.

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-08T13:15:38.361915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:15:38.361915Z digest=sha256:3c0d0ba6611f4aa09678d03f82b1c70b725a1b09fa56a83e72bd83d1950ff8ac

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.

source=pdf_text observed=2026-08-07T15:28:34.598403Z digest=sha256:83aee23d62adee99ac08cf39375cc4c74b5d7fb3c73dedbcf9adcad9af32051c

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.

source=pdf_text observed=2026-08-06T21:16:45.610444Z digest=sha256:120920898ab8fe22d60b0871059cec5087cf2792b9ca771d393e11a4c1e6604b

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.

source=pdf_text observed=2026-08-06T22:02:25.551918Z digest=sha256:ee021e8f1a65d3d67574747753c85e2c7b7ca00b067652d561876c33ec609a35

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T10:08:58.211032Z digest=sha256:26ddfc7208734bb5a2c68655f80a8445f09b1b68e819fc58fe07ad9bbb332e90