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Paper Citation Record · LEDGER

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.21913.

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

pith.paper-citation-record.v1
2606.21913 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:23:26.044644Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

28 of 28 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved25
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2d6e9b2e-e82a-4ce5-baec-74308c8f38c1 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 1

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:efa6be9b9013694916e19daebfb386b9f0476bdd015a76b1355184a168d11c70

Observation ccb51c3e-4d13-427f-91f6-e986ee3de6d4 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 2

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:a402f90a227d8370682faf3936203b8473ec891cd1982a4fb3e05a7c15f53320

Observation e423ab05-0e2b-4e82-af98-a019b7d616e8 · outbound

This paper cites IEEE Trans.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation IEEE Trans

Reference 3

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:47761641ec5a83b3daceb094b0e90dd49ae17d48738cff3adb88c960afa088ca

Observation aa373120-6645-4c73-95a1-02fc8c559a07 · outbound

This paper cites In: ICLR.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: ICLR

Reference 4

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:3b475dba0830acdba4107c5a8402943376cb40c6dcd5ea5db53a844882f78857

Observation 5949a2a9-91ce-4816-8cca-cb2c197e1b26 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 5

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:af14750b1e84430a062ef7655ba44429c91f9fd62dc1e6899454454f1e05c2b4

Observation deb4c913-0e8e-4e95-a3c0-81531e21364a · outbound

This paper cites Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 6

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local_arxiv, observed 2026-07-04T07:59:40.045783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:13e4387146d3187fd4c131b45409fdc4bab92e53dfbc426c75cd61dd67ae84b6

Observation 1dafe17f-238e-4d38-ba53-044feef92fe3 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 7

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:231756038c582b0960abdb5e29bad631ed511fcc608fa3584dfc96cb245695c9

Observation b999b8c8-ab9d-4d79-87ff-3aa4c7c46000 · outbound

This paper cites In: MIDL (2025).

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: MIDL (2025)

Reference 8

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:91473a8de897b398659f02089b083c575ab29a3932ae9ec1c356a95a775cfa0b

Observation edb3da30-b353-4c18-9711-7433aacc934f · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 9

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:e1e9b435da90dbdeb9118fb21b3f0415dcfd8d9023c927dd3ff8d2dc08649af3

Observation 592ce9f5-b1f1-403e-b543-3330fb93d6ae · outbound

This paper cites In: ICLR (2022).

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: ICLR (2022)

Reference 10

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:7360d9829f30db62b5a0150e70e1e784ed9fe8062523b6246c0ed21a80786bde

Observation 400869af-acde-4713-9f17-928a840f3373 · outbound

This paper cites In: ICCV.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: ICCV

Reference 11

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:b9596e71579e86d697e8ea1dcf847683025fba070f995e3d2c629da7d148ae3e

Observation 2dac4ab1-0a75-4f4d-a213-9b4f3867e1a9 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 12

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:4763536fc59f577c2c99c8657b46778ecc08af577f504085ded296cbe1b69973

Observation 15cc3f8b-054c-430b-94a7-01d502e5ab40 · outbound

This paper cites IEEE Transactions on Multimedia (2026).

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation IEEE Transactions on Multimedia (2026)

Reference 13

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:3cf13a9c25ec521824f7c86cb3dec3ab3ea7f98faf13d8288a7b045b02fb3999

Observation 36ce0b37-d353-43bc-a638-6a07b4767ddf · outbound

This paper cites In: MICCAI.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: MICCAI

Reference 14

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:50ec364ec27e023caf8bd30a0b8e5180d7cb86ab7d0f167fa06a16fb867010f9

Observation f8266fc7-8352-41b8-9dca-669549202faf · outbound

This paper cites IEEE Trans.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation IEEE Trans

Reference 15

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:f1fbbc693f914302e26f7fe16e32d9cf5b4e1eeb7be12eef690fd16dbfe056e6

Observation 18d9acef-e8aa-49c3-882e-4c4cb8f1b7a5 · outbound

This paper cites Knowl.-Based Syst.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Knowl.-Based Syst

Reference 16

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:aa99162b181b52352bf7fd21dbca83cdbc8eee0dafc676ecc153a1eb82be2c81

Observation 49c483c3-3edd-4584-a8de-cc0aee295164 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 17

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:5a0564bf5ec98325475c1702f485c1eca07e90f0990eb0816c2b0b3becbf464f

Observation 6cfcdfd1-8b85-420e-9e0d-90fcefefbdd9 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:af2b9469276e8fdaf16c41524a8b11dbf99a87bb963a7ad2d5f0db703897e5d7

Observation b57289a5-be27-4c14-809b-013b00e3768f · outbound

This paper cites IEEE Trans.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation IEEE Trans

Reference 19

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:2bf70a79c3fceb903e8e10f596d0bf779fdaf9d199fd918a301cc4819e0d48ae

Observation 82fc59c8-0ce2-4d31-84a7-52740a583515 · outbound

This paper cites Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation

Reference 20

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verified exact
arxiv_id, observed 2026-07-04T07:59:40.048243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:dc07f2fc4ea922e8bd3075700e27a956372ab208ea544f0dadec50420b9cfe67

Observation 2f3439b9-7500-40b3-9b1c-e1c96decd599 · outbound

This paper cites In: MICCAI.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: MICCAI

Reference 21

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:35bc3953d0578b0f8098c20a8d5209b177911e926ecc4e47d0f87eddbd4a68bd

Observation 4b5112af-3fc6-4f8b-902f-cf494102d218 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 22

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:1511c95fe3923c1b847d1c429f5199d25c929583da71db63d3dc2c1d6aa5290e

Observation 91468611-da42-4292-b278-d842cc759cf2 · outbound

This paper cites In: ECCV.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: ECCV

Reference 23

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:a088331bd83a1b0dd747fd4a42396620dc266f9f0497c5206ee2930f73d40072

Observation 7bfcd84f-0134-4f1a-9d35-bce7fb18904c · outbound

This paper cites DINOv3.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation DINOv3

Reference 24

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metadata mismatch
local_arxiv, observed 2026-07-04T07:59:40.050409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:e6a87c830207b2d7767986de0f8cfb28674bfbfccc31cf0182c66ae1b490c153

Observation 47f55a21-46ff-4c8f-a9d9-70250b8e2537 · outbound

This paper cites an unresolved cited work.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation Unresolved cited work

Reference 25

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:f620cc7fe6d52ff9664e8f59c345f2caccd163caf757f07a67f13d45ba2733cf

Observation c51fe433-f03c-400f-934b-ee2795124d89 · outbound

This paper cites In: MICCAI.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: MICCAI

Reference 26

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:1c812589a70d0e7eb948e087c62bf6b11953f730a4a0a85338da97da672138b9

Observation 0fbaa5cb-deff-4264-aaa8-477549e7983e · outbound

This paper cites IEEE Transactions on Medical Imaging (2025).

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation IEEE Transactions on Medical Imaging (2025)

Reference 27

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:f76036c609f03888b79c8282bc109d1dd60d5886f1af74c1e11b2fcd689974d4

Observation dca29eab-5942-4a82-897f-93f2bdb7613e · outbound

This paper cites In: ISBI.

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation In: ISBI

Reference 28

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source=pdf_text observed=2026-06-26T12:23:26.044644Z digest=sha256:9a8f98198307bee822d5913a1fb9d28580d34c030f5e5a4116f7d9f4c5443b93

Pith citing papers

No inbound Pith citation observations are available.