Paper Citation Record · LEDGER

Spatially-Aware Evaluation of Segmentation Uncertainty

As of 19 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 3 inbound Pith citation observations for arXiv:2506.16589.

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

pith.paper-citation-record.v1
2506.16589 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:41:23.504128Z

measured 19 of 19 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T21:24:56.512578Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:09:15.370663Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy10
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b88c48fd-156d-45f6-9457-c138e2b3e432 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

Spatially-Aware Evaluation of Segmentation Uncertainty The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.788676Z

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.

Observation 08c8b20d-48b9-447a-a563-5897e9f489e4 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Spatially-Aware Evaluation of Segmentation Uncertainty Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.434348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ce95f2-837a-435f-b090-e9ccdacbf3dc · outbound

This paper cites Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers.

Spatially-Aware Evaluation of Segmentation Uncertainty Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.439369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e92b823b-cdd6-48d3-9636-968b81585bff · outbound

This paper cites On calibration of modern neural networks.

Spatially-Aware Evaluation of Segmentation Uncertainty On calibration of modern neural networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.444735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58d3a96-ac94-49ed-b6de-ddf39b791414 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Spatially-Aware Evaluation of Segmentation Uncertainty A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.450429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54f4ce4c-b1b0-4286-9aa9-f8e4cb039f9f · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.

Spatially-Aware Evaluation of Segmentation Uncertainty nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.750141Z

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.

Observation df283596-7b74-4e3f-96a8-ed50f4f24619 · outbound

This paper cites Improving model calibration with accuracy versus uncertainty optimization.

Spatially-Aware Evaluation of Segmentation Uncertainty Improving model calibration with accuracy versus uncertainty optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.734690Z

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.

Observation 0c8974fb-49cf-4874-9a88-6e5faafc6689 · outbound

This paper cites Well-calibrated regression un- certainty in medical imaging with deep learning.

Spatially-Aware Evaluation of Segmentation Uncertainty Well-calibrated regression un- certainty in medical imaging with deep learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.716566Z

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.

Observation d75566a9-e870-404f-a64f-3ddfe9957213 · outbound

This paper cites Confidence calibration and predictive uncertainty estimation for deep medical im- age segmentation.

Spatially-Aware Evaluation of Segmentation Uncertainty Confidence calibration and predictive uncertainty estimation for deep medical im- age segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.698635Z

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.

Observation 128a644d-9c85-4fa9-b7d2-6217f5369ad3 · outbound

This paper cites Dropconnect is effective in modeling uncertainty of bayesian deep networks.

Spatially-Aware Evaluation of Segmentation Uncertainty Dropconnect is effective in modeling uncertainty of bayesian deep networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.682706Z

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.

Observation 5e11283c-79d9-4e84-9566-94b5320b351b · outbound

This paper cites Evaluating Bayesian Deep Learning Methods for Semantic Segmentation.

Spatially-Aware Evaluation of Segmentation Uncertainty Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.480129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584bc365-8cd4-466c-8301-bef99c855efa · outbound

This paper cites Accuracy-rejection curves (arcs) for com- paring classification methods with a reject option.

Spatially-Aware Evaluation of Segmentation Uncertainty Accuracy-rejection curves (arcs) for com- paring classification methods with a reject option

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.666415Z

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.

Observation 1c7008af-b021-43eb-9221-f1361d11f919 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

Spatially-Aware Evaluation of Segmentation Uncertainty Obtaining well calibrated probabilities using bayesian binning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.649472Z

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.

Observation 6f9a3f5b-5ce8-45a8-80cd-f003eb776761 · outbound

This paper cites Measuring calibration in deep learning.

Spatially-Aware Evaluation of Segmentation Uncertainty Measuring calibration in deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.632663Z

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.

Observation 8e4f155f-ec90-42e9-b8da-8b87693ac2c8 · outbound

This paper cites Monte-carlo frequency dropout for predic- tive uncertainty estimation in deep learning.

Spatially-Aware Evaluation of Segmentation Uncertainty Monte-carlo frequency dropout for predic- tive uncertainty estimation in deep learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:23.499523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18be9099-ca92-4d29-8d65-f4d45279bfd5 · outbound

This paper cites Staib, and John A.

Spatially-Aware Evaluation of Segmentation Uncertainty Staib, and John A

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:23.599199Z

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.

Pith citing papers

Observation 9b809dcf-7f0a-46a7-a805-9b5916f09f76 · inbound

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation cites this paper.

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation Spatially-Aware Evaluation of Segmentation Uncertainty

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T22:22:09.024670Z

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.

Observation a66ed379-6591-43e8-8d03-6ccbd07c6898 · inbound

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation cites this paper.

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation Spatially-Aware Evaluation of Segmentation Uncertainty

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:15:23.550960Z

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.

Observation 77f1120d-f580-49c9-92aa-9291b0f204d0 · inbound

Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation cites this paper.

Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation Spatially-Aware Evaluation of Segmentation Uncertainty

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:09:15.372823Z

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.