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Paper Citation Record · LEDGER

Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.08588.

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

pith.paper-citation-record.v1
2409.08588 v1

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:21:06.418469Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T06:00:00.184884Z

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 452793ca-b43d-4313-8510-51f477f6cc82 · inbound

Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN) cites this paper.

Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN) Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-12T11:21:06.418469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:21:06.418469Z digest=sha256:2301f219b1e90566c679c3810c61209ea22c6339374c5dfd54fc5182724c6995

Observation 7ecc6943-9c31-4063-afae-17c6fc3bdd94 · inbound

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network cites this paper.

DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-11T06:00:00.191361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:59:59.809777Z digest=sha256:d16c1239fd00e14011f358205a93c0aecb3105375dcc25e5a1f999698c8cb99a