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Paper Citation Record · LEDGER

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.12241.

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

pith.paper-citation-record.v1
1810.12241 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:46:03.698698Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T14:11:56.092656Z

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 448febf7-a0ac-43af-8ff2-7b0beb3f4d98 · inbound

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation cites this paper.

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-14T10:46:03.698698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:46:03.698698Z digest=sha256:37c9937eab489be4739ffad3e354991aeb6baaf9cbf95fa7bd460438e7306d40

Observation cb43e3cb-0999-4b40-b9f1-5077ed487d4b · inbound

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training cites this paper.

MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:11:56.097611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T14:11:55.871630Z digest=sha256:476c5872488a59d200628dca9fc447d3c4c46f17e9204b7ffa500178eaa2c773