Paper Citation Record · LEDGER

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts

As of 21 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2506.15153.

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

pith.paper-citation-record.v1
2506.15153 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:46:07.963387Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy5
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ebf41ba-74ff-4424-973d-fc0ed9d167ab · outbound

This paper cites ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.900889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de0c362d-edfc-4ebc-90c6-c9cd59a2e4bf · outbound

This paper cites IEEE Transactions on Medical Imaging43(6), 2202–2214 (2024).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts IEEE Transactions on Medical Imaging43(6), 2202–2214 (2024)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.905054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed03a8f4-d102-4d55-9f24-bf6415c2ab0e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.908472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb9f6636-0404-475a-aab6-92a6b2a45c6a · outbound

This paper cites Medical Image Analysis78, 102385 (2022).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Medical Image Analysis78, 102385 (2022)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.912091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fc31c32-6e92-4c6b-8ba3-d4d4c91be2a6 · outbound

This paper cites Medical image analysis69, 101950 (2021).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Medical image analysis69, 101950 (2021)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.915101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb2266da-813a-41c0-892f-633db9dccc85 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.918389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 823e1049-76df-4003-a356-5202dbcad258 · outbound

This paper cites In: Proc.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: Proc

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:46:08.116046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9226cd7e-4767-4ed5-afc7-0bc97e4af92f · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts DINOv2: Learning Robust Visual Features without Supervision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.926087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62f60b33-5823-49c5-8393-90b02cb49172 · outbound

This paper cites In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.929485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9535c747-fd4a-4e1f-bcd2-7eb32f0b6c47 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts SAM 2: Segment Anything in Images and Videos

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.932929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95457cb8-8e35-4bde-8af3-29ad08a2981b · outbound

This paper cites In: Proceedings of SAI Intelligent Systems Conference.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: Proceedings of SAI Intelligent Systems Conference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.936599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c96c071-285e-40ae-b980-45c8e02790cb · outbound

This paper cites International Journal of Computer Vision133(1), 1–15 (2025).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts International Journal of Computer Vision133(1), 1–15 (2025)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:46:08.094530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation edb0b76f-fe6d-4562-a48f-1142601291dd · outbound

This paper cites Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.943045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861806e9-1f09-466e-b541-6c2f4e057322 · outbound

This paper cites Advances in Neural Information Processing Systems (2017).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Advances in Neural Information Processing Systems (2017)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.946619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afffefaa-af78-480e-b607-d7dac9049cd5 · outbound

This paper cites In: European Conference on Computer Vision.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: European Conference on Computer Vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:46:08.078500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4802c851-7996-4ab6-9617-fbb3d1e612fd · outbound

This paper cites Biomedical Signal Processing and Control100, 107069 (2025).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Biomedical Signal Processing and Control100, 107069 (2025)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:46:08.068201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2795765c-4097-4a14-af6a-f3cb5003ecf3 · outbound

This paper cites Advances in Neural Information Processing Systems36 (2024).

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Advances in Neural Information Processing Systems36 (2024)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:46:08.057738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e40053e0-65ab-4c4f-999a-eeb9d61a5237 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Personalize Segment Anything Model with One Shot

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.959819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0727223-362c-4835-9e9a-b533946ad1ab · outbound

This paper cites In: International Conference on Med- ical Image Computing and Computer-Assisted Intervention.

SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts In: International Conference on Med- ical Image Computing and Computer-Assisted Intervention

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:46:07.963387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.