Paper Citation Record · LEDGER

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

As of 12 July 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2605.06891.

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

pith.paper-citation-record.v1
2605.06891 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:08:07.996357Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-12T06:30:05.999651+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T16:41:31.870327Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T16:47:24.403144Z

Reference resolution

14 of 14 outbound references displayed

verified exact9
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c9701e0-9e8c-44e4-a062-62b83c972db0 · outbound

This paper cites an unresolved cited work.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Unresolved cited work

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:10:28.143264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation a7896ef5-369b-4e75-97f1-4c201dcb23e1 · outbound

This paper cites Jessica Dai and Sarah M Brown.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Jessica Dai and Sarah M Brown

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:10:52.040958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 3efb4b33-8677-4acd-9c9d-620d324f58cc · outbound

This paper cites In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI)

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T01:10:52.035235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 8f8c57c9-593d-44cf-85a3-530e1f154303 · outbound

This paper cites Detecting labeling bias using influence functions.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Detecting labeling bias using influence functions

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.292774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 6e103b69-b573-4edd-a3e4-d08d0cffc8e2 · outbound

This paper cites Estimating label quality and errors in semantic segmentation data via any model.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Estimating label quality and errors in semantic segmentation data via any model

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.273328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 6611ebf5-bcc4-4ab1-bd85-770b91f7c4e8 · outbound

This paper cites Mitigating Label Bias via Decoupled Confident Learning.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Mitigating Label Bias via Decoupled Confident Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.264652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation f9d5e132-1fae-430e-9b0e-0791b5244c6a · outbound

This paper cites Investigating label bias and representational sources of age-related disparities in medical segmentation.arXiv preprint arXiv:2511.00477.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Investigating label bias and representational sources of age-related disparities in medical segmentation.arXiv preprint arXiv:2511.00477

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.283634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 099622a6-e370-4537-9b94-713a593826c8 · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Making deep neural networks robust to label noise: A loss correction approach

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:10:28.137998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation a96f2b39-7b9e-4407-a33d-2278cff8ff33 · outbound

This paper cites Are demographically invariant models and representations in medical imaging fair?.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Are demographically invariant models and representations in medical imaging fair?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.329239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 526eacc3-731a-499c-99d0-4b7ceeee2003 · outbound

This paper cites Common Limitations of Image Processing Metrics: A Picture Story.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Common Limitations of Image Processing Metrics: A Picture Story

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.322643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 00c764cb-0071-4315-be84-ff37229abcb1 · outbound

This paper cites Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:10:28.131809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation f1a54eee-05a9-4f81-8f79-ed00aa004afe · outbound

This paper cites That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.305958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 4447d80c-8625-4669-b428-1912f8664c21 · outbound

This paper cites Mitigating unwanted biases with adversarial learning.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Mitigating unwanted biases with adversarial learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T00:10:28.125421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation d10428da-dab2-4326-85f6-bc18e35b9158 · outbound

This paper cites De-biased Representation Learning for Fairness with Unreliable Labels.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation De-biased Representation Learning for Fairness with Unreliable Labels

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.313108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Pith citing papers

Observation ad19c6fe-87df-4803-a4f9-dba73ad8f369 · inbound

False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation cites this paper.

False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-10T16:47:24.404509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.