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Paper Citation Record · LEDGER

Deep Learning with Mixed Supervision for Brain Tumor Segmentation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1812.04571.

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

pith.paper-citation-record.v1
1812.04571 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:15:55.185044Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T09:35:35.487374Z

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 eea77d87-acd7-4851-bc00-422b3cc6f70a · inbound

Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks cites this paper.

Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks Deep Learning with Mixed Supervision for Brain Tumor Segmentation

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-25T09:35:35.490311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-25T09:33:13.973211Z digest=sha256:5ed94adb16b4bb0e564a9cd38bea6da2f072596d49f0d38404679183c8d088b8

Observation eed425b0-55e9-43a5-b8fc-bc6c2681c440 · inbound

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems cites this paper.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems Deep Learning with Mixed Supervision for Brain Tumor Segmentation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-14T13:15:55.185044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:15:55.185044Z digest=sha256:b9c07a89b1ac5db94f6ad3babda2435ed983a7c74f7f91ab78d0f73cc15d7ae9