Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.13220.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:02:25.017368Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:59:40.046985Z
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 6719182a-d142-4a65-be5c-e5189bae31a5 · inbound
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey cites this paper.
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:02:25.017368Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2288b4cb-de84-4c05-a836-9333a146de69 · inbound
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:56:03.707859Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9aa73bfc-bae9-48b6-b87b-942e87cb7c0e · inbound
Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation cites this paper.
Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T23:14:00.876990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb1a6b82-53e7-4493-a629-77f55a8dfa33 · inbound
Segment Anything for Cell Tracking cites this paper.
Segment Anything for Cell Tracking Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T18:27:53.406014Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82fc59c8-0ce2-4d31-84a7-52740a583515 · inbound
Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation cites this paper.
Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:59:40.048243Z
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.