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Paper Citation Record · LEDGER

A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2203.00131.

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

pith.paper-citation-record.v1
2203.00131 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:30:53.884007Z

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 eaabe570-4afc-496f-8306-2128232b9987 · inbound

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

Data-Centric Foundation Models in Computational Healthcare: A Survey A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 90

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:13:53.060925Z

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.

source=pdf_text observed=2026-05-24T04:13:05.328492Z digest=sha256:a1e200efeb74c4e1765d394e038353147c40ab8218baad7a9c207d670d4c0022

Observation 4d674fce-1018-4224-b1f3-f4ccbf0c3bff · inbound

Learning Segmentation from Radiology Reports cites this paper.

Learning Segmentation from Radiology Reports A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T19:30:53.884007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:30:53.884007Z digest=sha256:083f6beb29604aa261d3a41ec8c27e5d3874334e8ad92bfb39eebe04679c9523

Observation 8a78a213-8149-4f24-ae0d-9c80956b44b5 · inbound

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation cites this paper.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-04T19:27:19.403665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:27:19.403665Z digest=sha256:ef7f45b88927c4777e9894ca8df3a7fc57519f0ff070c19158760afdcaa0392a

Observation bb9edb25-5fa1-4f3b-b4d5-d8552955443b · inbound

DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation cites this paper.

DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T10:50:51.457597Z

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.

source=pdf_text observed=2026-05-16T10:49:00.846547Z digest=sha256:9ecddca7898058253b1f8aabf32f2a121e7f8a17ff3ff81b813a36172b9e3249

Observation 7ed7a175-ce98-43a8-b091-89cc5735b571 · inbound

CDSA-Net:Collaborative Decoupling of Vascular Structure and Background for High-Fidelity Coronary Digital Subtraction Angiography cites this paper.

CDSA-Net:Collaborative Decoupling of Vascular Structure and Background for High-Fidelity Coronary Digital Subtraction Angiography A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 39

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T07:26:59.207970Z

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.

source=pdf_text observed=2026-05-10T07:23:34.628440Z digest=sha256:f5b95cea76bdd36d5a8b4d75749a5bd87df478197a3d8a39e909181097f127d0

Observation c0e871c0-8ac6-49e4-bfc8-89620c531ff5 · inbound

MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation cites this paper.

MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:24:46.684793Z

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.

source=pdf_text observed=2026-05-10T00:24:45.639336Z digest=sha256:8ac7509a4e0217f7e6d22dfa0d7aae290694db7a8e59f7a45ed8c5c486483b2b

Observation cd1ff12b-838f-4b9c-91fa-deec44dc932a · inbound

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT cites this paper.

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-22T06:44:42.095991Z

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.

source=pdf_text observed=2026-05-22T06:41:48.446369Z digest=sha256:beb01cd0f99b9d8bf0ea31d3e1ebdf5232de935e88bc57f7c92c8a5dca5e6a81