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Paper Citation Record · LEDGER

Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation

As of 12 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2506.14497.

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

pith.paper-citation-record.v1
2506.14497 v1

Coverage vector

measured 3 of 3 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:24:12.022636Z

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

3 of 3 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d221962-5d15-42a9-8f91-5bce743f3a7d · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation MONAI: An open-source framework for deep learning in healthcare

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T00:24:05.702469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:24:05.702469Z digest=sha256:681453d7bf4eaa0ed656e239d831da4c196aa90a8647d931856e040fc98f754d

Observation bc97c2da-d672-47b9-970b-737913115877 · outbound

This paper cites (2018) A probabilistic U-Net for segmentation of ambiguous images.

Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation (2018) A probabilistic U-Net for segmentation of ambiguous images

Reference 48

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T00:24:12.385843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-07T00:24:11.796575Z digest=sha256:ce9be1faa6ac6b7bd445128afb5762174485d0f6de6adc9235c64157f90f4f06

Observation f33985f4-1bc4-4154-9609-92bdba831427 · outbound

This paper cites Springer, Cham.

Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation Springer, Cham

Reference 2023

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T00:24:12.240094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-07T00:24:12.022636Z digest=sha256:406cbc6918d4836ba4acec6c16c2c8f84ac3c0a31da76fd03b7e317c426fd9f5

Pith citing papers

No inbound Pith citation observations are available.