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Paper Citation Record · LEDGER

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation

As of 19 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2505.01951.

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

pith.paper-citation-record.v1
2505.01951 v2

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:08:57.573639Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

9 of 9 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved4
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acf56dbd-5145-480f-b4c4-bc5684c96fc4 · outbound

This paper cites M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-08-16T04:08:58.029450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T04:08:57.443472Z digest=sha256:13199253d2c2e740e7fe4d77bd004ad23b50b998704d4f1a8368bafa57a3e635

Observation ca2630f1-08df-4a7c-93aa-90e06049e09f · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-16T04:08:57.473641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:08:57.473641Z digest=sha256:f93c7b364b20c84a783f01911214ece70df52c83a19d2b5f698edf66d5f18cf3

Observation e9438cf9-fbdc-4792-a527-b40914c9dd40 · outbound

This paper cites An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor, MethodsX, vol.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor, MethodsX, vol

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:08:58.194345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T04:08:57.494825Z digest=sha256:89a2e47cd3cd7b24feeca8bf9c378756cb2698265fb9d01e76025e0daf9b59c6

Observation dc30572a-1c58-41b4-b1b0-ad94c879f7d6 · outbound

This paper cites Z., Che, H., Li, H., Qian, X.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation Z., Che, H., Li, H., Qian, X

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:08:58.112216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T04:08:57.501295Z digest=sha256:e26abf99bdfe03831a882a09d27c62dea6341852b4791744d712fffff61eb842

Observation c389c014-e7dc-43d0-bec4-257bf0711ca2 · outbound

This paper cites an unresolved cited work.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-16T04:08:57.509414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:08:57.509414Z digest=sha256:f9aa03dc1126ee2b36f5e54d9c19fb7f7afed6964672c3355311ed174268862e

Observation e8af67fa-6a61-4b52-9673-c1e8017692b6 · outbound

This paper cites TD-Net: Trans-Deformer network for automatic pancreas segmentation, Neurocomputing, vol 517, 2023, pp.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation TD-Net: Trans-Deformer network for automatic pancreas segmentation, Neurocomputing, vol 517, 2023, pp

Reference 6

Resolution
verified exact
doi, observed 2026-08-16T04:08:57.753363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T04:08:57.515101Z digest=sha256:42781ea15c6cf1ddbf9c316b3ffc253012e3c79cb3f740521dda90f501e9a528

Observation 6aad71d5-d624-4e59-b345-3c50b28a16ec · outbound

This paper cites An Introduction to Convolutional Neural Networks.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation An Introduction to Convolutional Neural Networks

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T04:08:57.522275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:08:57.522275Z digest=sha256:0bcd2be1740b3f6432d4811683a67bb1e433e5eeda0f3f0b1aab04987c1a389f

Observation 5c250b22-8d30-47a9-b386-4b5e8c1e4577 · outbound

This paper cites an unresolved cited work.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-16T04:08:58.049224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T04:08:57.531342Z digest=sha256:20db3fe3592c5605476c04d507f5a5e4c9aace0b765c2b0e4b9960da9b5414a6

Observation b5ecee98-c37c-4202-a8e4-9d63719f3f69 · outbound

This paper cites U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation.

UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

Reference 9

Resolution
malformed identifier
no resolver link, observed 2026-08-16T04:08:57.573639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:08:57.573639Z digest=sha256:b9f60224db4fc021fb8943b282887fde4c6c39d14dd98cdc50c0e8ccebdb3646

Pith citing papers

No inbound Pith citation observations are available.