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Paper Citation Record · LEDGER

U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.06104.

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

pith.paper-citation-record.v1
2103.06104 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:35:15.896781Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:24:15.363026Z

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 e3d2304d-9a1a-4af3-9231-522321603566 · inbound

Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers cites this paper.

Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:15.896781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:35:15.896781Z digest=sha256:7a3f2cb056042d6b4cb30f515e99fa8a0fd7dc02db2aee65e771ae704b2832a4

Observation b20cd2e8-d3ed-42ed-8e8c-247719c05a8f · inbound

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images cites this paper.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:24:15.517822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:24:09.298065Z digest=sha256:e05c5d8da15bf342dee987441d85cf02c3470037f34fcb075eed916e37371fa8