Paper Citation Record · LEDGER

Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2304.09324.

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

pith.paper-citation-record.v1
2304.09324 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:02:15.013156Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

65
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b3405732-d7ac-4c9e-8258-f0ea0819c462 · inbound

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

Data-Centric Foundation Models in Computational Healthcare: A Survey Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 109

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:13:52.943484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5e21e778-6fda-49d8-8017-7709597aa0b9 · inbound

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements cites this paper.

ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T18:02:15.013156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a46fbb86-761b-4dae-a87a-9f0d65348729 · inbound

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network cites this paper.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:51:32.857155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee1fe2d8-9a59-410b-908d-431774c480d7 · inbound

Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities cites this paper.

Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T05:13:07.710145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efac9434-d729-471c-bd5b-565fba4cb7a7 · inbound

Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning cites this paper.

Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:49:35.089300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346058f7-d4a1-4e3f-9dc3-ea4d6320a14a · inbound

PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation cites this paper.

PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T20:59:06.953092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a801b799-5a1d-44b6-8d35-e4e9443ba8ad · inbound

Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes cites this paper.

Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T05:45:27.975975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 412b69be-84f8-43fe-aa63-2a92b85082fb · inbound

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models cites this paper.

ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T05:23:20.248723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c53d113a-1ef9-4080-be38-65638d32a7b2 · inbound

CardioSAM: Topology-Aware Decoder Design for High-Precision Cardiac MRI Segmentation cites this paper.

CardioSAM: Topology-Aware Decoder Design for High-Precision Cardiac MRI Segmentation Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:46:13.318922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ccb47fa-5be3-4bd4-b02e-4ef1c15d0ad9 · inbound

Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models cites this paper.

Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:01.484968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a79af503-2d9e-4c9a-9fb2-3f437a8f472e · inbound

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

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:17:57.660220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 234b5387-5f3c-4292-b96b-98572b3d8c66 · inbound

MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network cites this paper.

MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:19:57.878553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cf1e305d-3d33-4539-8c20-1116ef0c6dc3 · inbound

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation cites this paper.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:31.678291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.