Paper Citation Record · LEDGER

When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2304.08506.

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

pith.paper-citation-record.v1
2304.08506 v6

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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:28:35.656427Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:57.656664Z

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 ea449b9e-9a86-4127-9805-21248b5f32ea · inbound

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

Data-Centric Foundation Models in Computational Healthcare: A Survey When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 115

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:13:52.569070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ffa26394-c81f-47bc-ae5f-cb6687ef2226 · inbound

On Efficient Variants of Segment Anything Model: A Survey cites this paper.

On Efficient Variants of Segment Anything Model: A Survey When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.509306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 45539148-4988-4bd7-81aa-b5ed78b11f85 · inbound

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

TAGS: 3D Tumor-Adaptive Guidance for SAM When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:28:35.656427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d91a8249-89d9-4eb6-8df1-881910e6de7b · inbound

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

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 175

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:25.547144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea2cacef-89e8-402b-bc32-6875f05caa33 · inbound

Do Instance Priors Help Weakly Supervised Semantic Segmentation? cites this paper.

Do Instance Priors Help Weakly Supervised Semantic Segmentation? When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T08:56:03.233574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b26fbad-8289-4d02-a64e-5e75171fcd44 · inbound

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

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:17:57.657994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.