Paper Citation Record · LEDGER

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

As of 22 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2606.23177.

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

pith.paper-citation-record.v1
2606.23177 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:17:01.445278Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy0
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69a2f3e1-0e17-40c8-b470-8968463bf467 · outbound

This paper cites Nature communications13(1), 4128 (2022).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Nature communications13(1), 4128 (2022)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2770708-4d34-458a-9ab6-590933e0dfbc · outbound

This paper cites In: International conference on medical image computing and computer-assisted intervention.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: International conference on medical image computing and computer-assisted intervention

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b95258a1-ff92-4121-a553-da082f9239eb · outbound

This paper cites an unresolved cited work.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Observation 31765e90-ed1b-429f-ab14-dca0b3c67429 · outbound

This paper cites Information Sciences545, 771–790 (2021).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Information Sciences545, 771–790 (2021)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Unavailable: canonical work link unavailable.

Observation 51fa6a9f-c8be-4b15-a1db-556ef6a95776 · outbound

This paper cites IEEE transactions on medical imaging39(11), 3679–3690 (2020).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability IEEE transactions on medical imaging39(11), 3679–3690 (2020)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Observation 2a4f1b49-0a72-4e2a-b69a-c006c871b265 · outbound

This paper cites In: Ad- vances in Neural Information Processing Systems (2018).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Ad- vances in Neural Information Processing Systems (2018)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Observation f4c9c166-c2ab-4366-b114-cd1b73504d20 · outbound

This paper cites Journal of the American statistical Association102(477), 359–378 (2007).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Journal of the American statistical Association102(477), 359–378 (2007)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04c5b60-c6c3-492d-a349-a3f4fc1d9bb6 · outbound

This paper cites Medical Image Analysis71, 102053 (2021).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Medical Image Analysis71, 102053 (2021)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 700b60b2-f984-493e-a9a4-8ce3449cd23b · outbound

This paper cites In: International conference on machine learning.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: International conference on machine learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7979adfd-f3bb-4185-a086-5450c340339e · outbound

This paper cites Gaussian Processes for Big Data.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Gaussian Processes for Big Data

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c42ca77b-ccf8-40c8-8463-2df2e3628e71 · outbound

This paper cites In: Artificial intelligence and statistics.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Artificial intelligence and statistics

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

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Observation c9fb8cb2-2a90-42ad-8743-8d990551696c · outbound

This paper cites In: Inter- national Conference on Medical Image Computing and Computer-Assisted Inter- vention.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Inter- national Conference on Medical Image Computing and Computer-Assisted Inter- vention

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Unavailable: canonical work link unavailable.

Observation bf90e7c8-e697-407c-8c8d-35accd215728 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2efc44d0-f096-4e86-9407-9cea038f32db · outbound

This paper cites an unresolved cited work.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9f03c4-cfbd-4cee-ab45-145b84871a7b · outbound

This paper cites Advances in neural information processing sys- tems31(2018).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Advances in neural information processing sys- tems31(2018)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 550a9343-1878-4716-a43b-3c759b63c0f2 · outbound

This paper cites A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T09:59:45.409067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b618c13a-9415-4ef2-9d7a-5b5c749ccc1e · outbound

This paper cites BMC Research Notes15(1), 210 (2022).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability BMC Research Notes15(1), 210 (2022)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Unavailable: canonical work link unavailable.

Observation cd5f37f9-4207-4c83-aed5-6793aa9edf27 · outbound

This paper cites Journal of Machine Learning Research9(Oct), 2035–2078 (2008).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Journal of Machine Learning Research9(Oct), 2035–2078 (2008)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ddafd73-7a29-4b73-8c68-f07f322cc9fd · outbound

This paper cites In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e36e49d6-c6e3-4586-9a47-6d0636b4e067 · outbound

This paper cites In: Proceedings of the AAAI conference on artificial intelligence.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Proceedings of the AAAI conference on artificial intelligence

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3231e662-b814-4a7b-82f0-f6cf2756177f · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 571f5751-120f-420f-ba2f-cf4f9339ce1f · outbound

This paper cites Ad- vances in neural information processing systems18(2005).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Ad- vances in neural information processing systems18(2005)

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02820f04-90c5-4df2-ad58-a2f04421190d · outbound

This paper cites NPJ Digital Medicine6(1), 26 (2023).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability NPJ Digital Medicine6(1), 26 (2023)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccf1d0fa-3e42-4a84-84a2-a4f79da578e1 · outbound

This paper cites In: Artificial intelligence and statistics.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Artificial intelligence and statistics

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03e97fd6-7147-455d-8c70-256d4355259d · outbound

This paper cites Nature communications12(1), 5915 (2021).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Nature communications12(1), 5915 (2021)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7881aa1-4941-43d5-80e3-e81d9c6327a1 · outbound

This paper cites Advances in Neural Information Processing Systems 34, 13230–13241 (2021).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Advances in Neural Information Processing Systems 34, 13230–13241 (2021)

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Unavailable: canonical work link unavailable.

Observation 6551ca3c-010f-4172-8e39-59c6aafb87e5 · outbound

This paper cites an unresolved cited work.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5892af3-9eb2-443a-9cdc-afe0d4da7944 · outbound

This paper cites In: Artificial intelligence and statistics.

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: Artificial intelligence and statistics

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Observation 92ba4a68-d2ce-4a28-a0e1-be61a783d2a4 · outbound

This paper cites European journal of nuclear medicine and molecular imaging37(11), 2165–2187 (2010).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability European journal of nuclear medicine and molecular imaging37(11), 2165–2187 (2010)

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Observation 51703913-1fde-4128-b891-cdde913cdea3 · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2023).

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability In: The Eleventh International Conference on Learning Representations (2023)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b017a70f-578a-45a8-8f9d-51f4843b9210 · outbound

This paper cites Advances in Neural Information Processing Systems33, 15750– 15762 (2020).

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T09:17:01.445278Z

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Pith citing papers

No inbound Pith citation observations are available.