Paper Citation Record · LEDGER

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.12602.

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

pith.paper-citation-record.v1
2411.12602 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:26:01.409866Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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 exact1
verified fuzzy12
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e7940784-3bb0-4402-aa9b-f0a7f9e6896b · outbound

This paper cites In: 2014 11th International Conference on Electronics, Computer and Computation (ICECCO).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: 2014 11th International Conference on Electronics, Computer and Computation (ICECCO)

Reference 1

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T17:26:01.845817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d159a9a3-0f9a-4612-824e-0d47cb7c98cb · outbound

This paper cites In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.305790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85c298a9-15ad-45ca-829c-983fdaf24a56 · outbound

This paper cites In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:02.042430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62a6b45a-7b5e-47a4-a76a-66b1245355b0 · outbound

This paper cites Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.313696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5560193f-adb9-4378-b2b4-a81d15f55d86 · outbound

This paper cites an unresolved cited work.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:26:02.029528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f4b2828-926a-4bd2-8228-e07268981883 · outbound

This paper cites MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T17:26:01.712044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 533f1e06-d5ac-452f-8575-7a7b77d8011f · outbound

This paper cites Multimedia Tools and Applications pp.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Multimedia Tools and Applications pp

Reference 7

Resolution: verified exact; doi, observed 2026-08-12T17:26:01.452896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab2e6ad7-eca1-4a8b-afb6-c6a050f5301d · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1768–1783 (2006).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1768–1783 (2006)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:02.018686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd703a64-572c-446b-8f6b-fbf22ce94694 · outbound

This paper cites In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (June 2016) 10 Keuth et al.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (June 2016) 10 Keuth et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:02.006832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c1a9c2c0-5fda-4ca5-8ee3-b8a720381091 · outbound

This paper cites In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (October 2019).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (October 2019)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.993152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99213a94-29ad-40b6-9cc8-1e1b4d000ef3 · outbound

This paper cites https://doi.org/https://doi.org/10.1016/j.media.2023.103061, https://www.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation https://doi.org/https://doi.org/10.1016/j.media.2023.103061, https://www

Reference 11

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T17:26:01.695146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0e764f3-31f4-4aa2-8fb6-cddffcc265b4 · outbound

This paper cites an unresolved cited work.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:26:01.981579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 60f0f088-bc45-47fa-9e3a-dcb2fdedcb22 · outbound

This paper cites https://doi.org/10.1038/s41592-020-01008-z.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation https://doi.org/10.1038/s41592-020-01008-z

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.348663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c55a7bac-ced1-4960-8fbf-47b557d9499d · outbound

This paper cites Computers in biology and medicine 169, 107840 (2022).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Computers in biology and medicine 169, 107840 (2022)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.970569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 799a1eaa-205e-4f27-9a25-d416fb4d2dfe · outbound

This paper cites 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.959173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61d00a5e-e54c-42ee-8ad5-fbe6a1a1a059 · outbound

This paper cites In: Inter- national Conference on Learning Representations (2017), https://openreview.net/ forum?id=BJ6oOfqge.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Inter- national Conference on Learning Representations (2017), https://openreview.net/ forum?id=BJ6oOfqge

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.947992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1425b06b-ffca-486d-a5d9-0af9979fd9c4 · outbound

This paper cites ICML 2013 Workshop : Challenges in Representation Learning (WREPL) (07 2013).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation ICML 2013 Workshop : Challenges in Representation Learning (WREPL) (07 2013)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.363714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d30fa8b0-8c5e-4948-9937-8c8d4a7336a2 · outbound

This paper cites In: International Conference on Neural Information Processing (2023).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: International Conference on Neural Information Processing (2023)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.930996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6abbaa7a-02d2-4f87-88f9-0127f1b618d3 · outbound

This paper cites https://doi.org/10.34740/KAGGLE/DSV/5884500, https://www.kaggle.com/ dsv/5884500.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation https://doi.org/10.34740/KAGGLE/DSV/5884500, https://www.kaggle.com/ dsv/5884500

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.370582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8099d64e-3b79-4de5-92ea-6c3b2492831e · outbound

This paper cites Nature Communications 15(1), 654 (2024).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Nature Communications 15(1), 654 (2024)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.374156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f919956d-cd89-484a-8105-2c1e4e1252c7 · outbound

This paper cites https://doi.org/10.1038/s41597-022-01328-z, https://www.nature.com/ articles/s41597-022-01328-z.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation https://doi.org/10.1038/s41597-022-01328-z, https://www.nature.com/ articles/s41597-022-01328-z

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.377650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 827225c8-4604-4df1-8282-4d08f6b767a6 · outbound

This paper cites In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.381578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0867ce69-b9ae-4054-aec1-f1ed21d5feb1 · outbound

This paper cites ACM SIGGRAPH 2004 Papers (2004).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation ACM SIGGRAPH 2004 Papers (2004)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.899582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a055283d-ea8d-4b84-9388-88a01f5985a6 · outbound

This paper cites Advances in neural information processing systems 30 (2017) SAM Carries the Burden 11.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Advances in neural information processing systems 30 (2017) SAM Carries the Burden 11

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.888592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c5d518da-3eb1-4ad3-b46d-9a513af9d566 · outbound

This paper cites IEEE transactions on medical imaging 37(7), 1562–1573 (2018).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation IEEE transactions on medical imaging 37(7), 1562–1573 (2018)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.395016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7c39a7-b265-483e-92f1-3a9a5a5afbec · outbound

This paper cites SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.398527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19fe5102-1a8a-4050-a023-ede7183d0972 · outbound

This paper cites Computers in biology and medicine 171, 108238 (2024).

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Computers in biology and medicine 171, 108238 (2024)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.869505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 093ac046-cb7b-4ef4-b7fd-d81995f1c056 · outbound

This paper cites SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Reference 28

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T17:26:01.469244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab42d3b4-717f-4e5a-9971-fe15eba6523a · outbound

This paper cites In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:26:01.857459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 70ce4c86-2fa4-44f3-be96-d46a31ca6555 · outbound

This paper cites an unresolved cited work.

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation Unresolved cited work

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-12T17:26:01.385058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.