Paper Citation Record · LEDGER

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.09829.

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

pith.paper-citation-record.v1
2505.09829 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:28:18.687879Z

measured 30 of 30 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

30 of 30 outbound references displayed

verified exact0
verified fuzzy14
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7a3e29c9-80af-4719-a4dd-e00285122374 · outbound

This paper cites Ieee Access8, 179424–179436 (2020).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Ieee Access8, 179424–179436 (2020)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.469730Z

Source-reported events for the cited work

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Observation 95716ba3-63ed-4e1e-8f88-587d66cc6d9b · outbound

This paper cites In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.455182Z

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Observation 321faffb-c12d-4fcb-9fdf-42a4e2679467 · outbound

This paper cites Biotechnology Reports22, e00321 (2019).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Biotechnology Reports22, e00321 (2019)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.442226Z

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Observation 163d3381-46c7-4f43-a074-02702ef173dd · outbound

This paper cites In: International conference on machine learning.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: International conference on machine learning

Reference 4

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Observation 0b4b191b-8d33-4f3a-836c-8e6431e22bd4 · outbound

This paper cites Journal of Medical Imaging6(2), 025503–025503 (2019).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Journal of Medical Imaging6(2), 025503–025503 (2019)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.421118Z

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Observation 347ee386-0fb4-4091-a232-2a2d61ba3eba · outbound

This paper cites Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.318613Z

Source-reported events for the cited work

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Observation 621c9dc3-6b85-455b-bcff-da61d2681e4a · outbound

This paper cites Advances in Neural Information Processing Systems34, 27503–27516 (2021).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Advances in Neural Information Processing Systems34, 27503–27516 (2021)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.408734Z

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 bacb40c2-cd97-4ed4-a117-45dde25ac7e6 · outbound

This paper cites Journal of Medical Imaging10(2), 024007–024007 (2023).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Journal of Medical Imaging10(2), 024007–024007 (2023)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.378198Z

Source-reported events for the cited work

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Observation 6f43aaf5-e7dc-4d4d-9a04-a2eca64065d0 · outbound

This paper cites In: Medical Imaging with Deep Learning.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Medical Imaging with Deep Learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.253150Z

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 e43c5888-3420-4d69-a108-b664a3127622 · outbound

This paper cites StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.340907Z

Source-reported events for the cited work

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Observation 702b333e-a138-466e-b2ce-78a368ef1fc3 · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 142e8fd6-c8df-46e1-91d0-cd97594054b8 · outbound

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

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Proceedings of the AAAI conference on artificial intelligence

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.489779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5b27cc-9216-45da-904e-7acd7e7a3dd3 · outbound

This paper cites Clinical Radiology78(2), 115–122 (2023) 10 T.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Clinical Radiology78(2), 115–122 (2023) 10 T

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.207407Z

Source-reported events for the cited work

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Observation d29cb8dc-a22d-47d5-8410-80aced2ae94b · outbound

This paper cites In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, Octo- ber 17-21, 2016, Proceedings, Part II 19.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, Octo- ber 17-21, 2016, Proceedings, Part II 19

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.194488Z

Source-reported events for the cited work

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Observation 0e27cd37-a207-4cc3-830a-4fc130ee756d · outbound

This paper cites Sensors21(17), 5855 (2021).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Sensors21(17), 5855 (2021)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.179570Z

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 9ad38745-e3c7-4c7b-8cd1-e9ba8959aa18 · outbound

This paper cites International journal of computer vision115, 211–252 (2015).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes International journal of computer vision115, 211–252 (2015)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.512297Z

Source-reported events for the cited work

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Observation 9145a027-3087-4268-8928-e03d159197cb · outbound

This paper cites Ad- vances in neural information processing systems31(2018).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Ad- vances in neural information processing systems31(2018)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.516703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e17397f8-cb1d-4353-a8f4-3f40c995afec · outbound

This paper cites Label Encoding for Regression Networks.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Label Encoding for Regression Networks

Reference 18

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Observation 3e4da578-2314-4bcd-a2bf-64a030a7c584 · outbound

This paper cites an unresolved cited work.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:28:19.149048Z

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 2fbbd106-aeca-4d49-8213-cfc6bded8654 · outbound

This paper cites In: International Conference on Artificial Intelligence and Statistics.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: International Conference on Artificial Intelligence and Statistics

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.134282Z

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 889a6a14-1d93-4843-ae1e-59f1276c8139 · outbound

This paper cites Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment

Reference 21

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Observation 972e51f4-43fd-4a55-8d80-6a00a760fa22 · outbound

This paper cites On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling

Reference 22

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Observation da2bad22-91ff-4041-9b98-66f99889b6b8 · outbound

This paper cites ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.656741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d6a199-7312-4e1d-816b-db389ae36f13 · outbound

This paper cites Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.661379Z

Source-reported events for the cited work

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Observation c6a31f1e-dec6-4506-9f19-91ad6ab6af70 · outbound

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

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: International conference on medical image computing and computer-assisted intervention

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.666447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c89a3d63-ed44-4f28-93d2-658edfb5f7e4 · outbound

This paper cites Medical image analysis67, 101832 (2021).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Medical image analysis67, 101832 (2021)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.109286Z

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 aa095a5a-8377-4b4e-8c28-a5c5a01c3eae · outbound

This paper cites Artificial intelligence in medicine143, 102607 (2023).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Artificial intelligence in medicine143, 102607 (2023)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.095284Z

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 655b9283-b192-4ed8-8b5e-90796d5fa81b · outbound

This paper cites Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.679903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04bf2ee5-0aef-43c5-98b5-06cdc7bbbaf1 · outbound

This paper cites In: Medical Image Comput- ingandComputerAssistedIntervention–MICCAI2019:22ndInternationalConfer- BoundarySeg11 ence, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22.

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes In: Medical Image Comput- ingandComputerAssistedIntervention–MICCAI2019:22ndInternationalConfer- BoundarySeg11 ence, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:28:19.018475Z

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 654aa537-20b0-4d39-a880-d62c0c8bc433 · outbound

This paper cites Advances in neural information processing systems33, 5824–5836 (2020).

BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes Advances in neural information processing systems33, 5824–5836 (2020)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:28:18.687879Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.