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Paper Citation Record · LEDGER

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT

As of 7 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2606.27084.

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

pith.paper-citation-record.v1
2606.27084 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T05:04:51.962808Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

13 of 13 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab5b605c-0103-4229-af55-a0820905cb23 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

Reference 1

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:420697c5c45455860b5ad8a28eb66b239e30c141f489e47f119fc5f162275f77

Observation 2fed3410-c98c-4d5b-b8f1-84a24c60d04f · outbound

This paper cites Exemplar med-detr: Toward generalized and robust lesion detection in mammogram images and beyond.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Exemplar med-detr: Toward generalized and robust lesion detection in mammogram images and beyond

Reference 2

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unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:1a243b515e241d9bc0fad68639fae78c5ca8f1a5414583231491980ddaf5c3b8

Observation cd9e4a07-b582-411f-ab06-5439db45cf85 · outbound

This paper cites End-to-end object detection with transformers.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT End-to-end object detection with transformers

Reference 3

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unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:6ae305b53d19c91ad7f7203046d8f33971cc54a6a15dc90ae7faccf3f97eb803

Observation ba514dd0-bd19-4523-b9c9-540cc96a3c2d · outbound

This paper cites Rsna 2023 abdominal trauma ai challenge: Review and outcomes.Radiology: Artificial Intelligence, 7(1):e240334, 2024.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Rsna 2023 abdominal trauma ai challenge: Review and outcomes.Radiology: Artificial Intelligence, 7(1):e240334, 2024

Reference 4

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unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:8cbc06b5d030ed0b344fca1b53fcca5789e12466c15f9a228f8f27c29a0a4327

Observation ed402d16-803d-450b-82e2-4067fe120144 · outbound

This paper cites arXiv preprint arXiv:2404.15272 (2024).

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT arXiv preprint arXiv:2404.15272 (2024)

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:39:50.441059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:6659947b2cc19d8640845c0ed92783383ecec4186d30712c4fc096261963de20

Observation 077eedb4-1193-4869-a399-80afbeb0517a · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 6

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:3489fd2eafdd4f78e5e07a8011b108d17322e7b81a39eae7f19d67e62dfdf585

Observation 8948f2d6-6440-4f17-af3d-8072a1a46a91 · outbound

This paper cites Deepcut: Object segmentation from bounding box annotations using convolutional neural networks.IEEE transactions on medical imaging, 36(2):674–683, 2016.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Deepcut: Object segmentation from bounding box annotations using convolutional neural networks.IEEE transactions on medical imaging, 36(2):674–683, 2016

Reference 7

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unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:f74f8a32e02a19c5af6f511159e3503563edf52515991dc109986e36e5bbbea2

Observation 93479111-2fad-471a-93b3-10bed3b25c37 · outbound

This paper cites The rsna abdominal traumatic injury ct (ratic) dataset.Radiology: Artificial Intelligence, 6(6):e240101, 2024.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT The rsna abdominal traumatic injury ct (ratic) dataset.Radiology: Artificial Intelligence, 6(6):e240101, 2024

Reference 8

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:4a4db0b02df020fd617107d08db8b92142aa54e326d039837c72ed01e08689f0

Observation 88b8d919-e141-4a7b-951d-70d7a37f2089 · outbound

This paper cites Totalsegmentator: robust segmentation of 104 anatomic structures in ct images.Radiology: Artificial Intelligence, 5(5):e230024, 2023.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Totalsegmentator: robust segmentation of 104 anatomic structures in ct images.Radiology: Artificial Intelligence, 5(5):e230024, 2023

Reference 9

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:5b6c4572cd2b8554bc597973df4f429f8270466a6524656cc30dba392925666f

Observation 15ae74ec-333d-4410-8534-cf1e2caf4c7b · outbound

This paper cites Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.Journal of medical imaging, 5(3):036501–036501, 2018.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.Journal of medical imaging, 5(3):036501–036501, 2018

Reference 10

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:2f212f211e854679597c335fc9e4cfcf1bf4c4d424a5798f20079e6d48a7bfb2

Observation ad892f77-9ac6-405c-b0bc-7c86d53ccc51 · outbound

This paper cites Swin3d: A pretrained transformer backbone for 3d indoor scene understanding.Computational Visual Media, 11(1):83–101, 2025.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Swin3d: A pretrained transformer backbone for 3d indoor scene understanding.Computational Visual Media, 11(1):83–101, 2025

Reference 11

Resolution
unresolved
no resolver link, observed 2026-06-26T05:04:51.962808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:7c8b0b8572204056da97c48e1b00ed7298f9054a9267c69cfa0052d873e5383e

Observation 1a106f6e-9605-44f2-bbc2-bd1d63e03761 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-07-04T13:39:50.438191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:515ec3b6f898a4e0fa2b4ab0c5a26f16609451470a5f802d6d8328c9a805a015

Observation c9050676-2577-4a55-a632-7335d692ee7e · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-07-04T13:39:50.435191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T05:04:51.962808Z digest=sha256:78cf874926c27879982c097002add3e846c92fa56f584a1a960caf2523ae3258

Pith citing papers

No inbound Pith citation observations are available.