Paper Citation Record · LEDGER

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2412.19990.

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

pith.paper-citation-record.v1
2412.19990 v2

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

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:19:39.292875Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T05:41:02.378249Z

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 f3c5753f-0714-47fa-9c08-3035b0bb4e4a · inbound

MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction cites this paper.

MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:39.292875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 742a8a50-5134-4946-89fa-d661117123cd · inbound

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation cites this paper.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T19:33:21.506141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72bdedbd-7a1b-497b-982d-207959a52b16 · inbound

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation cites this paper.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:35.609588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 864d0c9b-9a2f-4332-81a0-5713c399b814 · inbound

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation cites this paper.

SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.379597Z

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.