Paper Citation Record · LEDGER

How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.09957.

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

pith.paper-citation-record.v1
2404.09957 v3

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 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:55:04.401040Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T17:52:46.200810Z

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 b0b11b17-3086-498a-bb5a-b90a7feb89f8 · inbound

SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI cites this paper.

SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:04.401040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3386c90a-d839-42bd-a23b-7f60c1626175 · inbound

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration? cites this paper.

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration? How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:27:03.612406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905be7bf-c1d2-4bbe-b34e-e4489f6cbfe9 · inbound

MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation cites this paper.

MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:52:46.203942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c31dcb3-87ae-4ac0-b7cf-60d355fe3efc · inbound

From pre-training to downstream performance: Does domain-specific pre-training make sense? cites this paper.

From pre-training to downstream performance: Does domain-specific pre-training make sense? How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:37:08.284176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.