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Paper Citation Record · LEDGER

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation

As of 9 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2607.29568.

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

pith.paper-citation-record.v1
2607.29568 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:29:05.752620Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

55 of 55 outbound references displayed

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  • verified fuzzy0
  • unresolved54
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fec061a9-dc9f-40a5-a5ce-c39fd920af18 · outbound

This paper cites Magnetic resonance imaging , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Magnetic resonance imaging , volume=

Reference 1

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source=arxiv_source observed=2026-08-03T04:29:00.776619Z digest=sha256:c35a935d07d54a8559243be81a4cd2465e89f88a83ab6eb4a415df8f9d37e6ae

Observation 2828d09c-69e2-469f-954c-05e3a1ba1b92 · outbound

This paper cites Nature medicine , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Nature medicine , volume=

Reference 2

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source=arxiv_source observed=2026-08-03T04:29:00.923719Z digest=sha256:b972eb0baa4717ca3694320b19a1aadf1995fb60f7060ba8c3642ab6e4364353

Observation f2a83802-c52e-48d8-98dc-b3b963bad0e1 · outbound

This paper cites International Conference on Medical image computing and computer-assisted intervention , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation International Conference on Medical image computing and computer-assisted intervention , pages=

Reference 3

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source=arxiv_source observed=2026-08-03T04:29:01.038906Z digest=sha256:18f70d02534ab82376389c5739bb8374d9596bb8580d04dfe13882b3de29d8b6

Observation c035d678-a073-402d-950f-8dba4cd23cfa · outbound

This paper cites 2016 fourth international conference on 3D vision (3DV) , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation 2016 fourth international conference on 3D vision (3DV) , pages=

Reference 4

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source=arxiv_source observed=2026-08-03T04:29:01.154224Z digest=sha256:7208ce03e8046f51d0254eb33df900a69446c11a1b5dadd8e7cb8c2b1653efff

Observation 9da8f9c8-9dcf-4c48-86d9-e71d0c8d0457 · outbound

This paper cites Journal of hepatology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Journal of hepatology , volume=

Reference 5

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source=arxiv_source observed=2026-08-03T04:29:01.264512Z digest=sha256:5cc564032a74e955b09f89aafd86fa1f75f897b0ec16a66f16d2cd419015b8ee

Observation 8930f1a7-b7a0-4966-801e-0b995808d124 · outbound

This paper cites Radiology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Radiology , volume=

Reference 6

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source=arxiv_source observed=2026-08-03T04:29:01.407748Z digest=sha256:acc388731baf193bb20b97ddb48e45294b7ebba6e85334f55af141ecd8de2d69

Observation 4538c959-22bd-4e2b-9c88-7c42eb933c89 · outbound

This paper cites Cancer Imaging , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Cancer Imaging , volume=

Reference 7

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source=arxiv_source observed=2026-08-03T04:29:01.561259Z digest=sha256:1be512159417087ba976daca9c7147a729c3bcd83961ae29a480d6ea3712d589

Observation 7ad4d92a-e7a0-4f2b-9127-19cfc9ccd54c · outbound

This paper cites Best Practice & Research Clinical Gastroenterology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Best Practice & Research Clinical Gastroenterology , volume=

Reference 8

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source=arxiv_source observed=2026-08-03T04:29:01.704052Z digest=sha256:903737f898678543b79a53d73fc658236521fd2384dd11691198aace3c8d576c

Observation 07f5d5f3-7be3-4363-82b2-aa83f6db86b2 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 9

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source=arxiv_source observed=2026-08-03T04:29:01.805342Z digest=sha256:39c17a678581eaaf06689e2aae228265516e5e28cda5a7a2b24dec37bf533fef

Observation 5c6f961c-84cb-40cb-8aa2-e8ae42bd005c · outbound

This paper cites Array , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Array , volume=

Reference 10

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source=arxiv_source observed=2026-08-03T04:29:01.896777Z digest=sha256:1387a67016154ca46567825a8e61458d4f5091e1420a73a4bc82471878ac3ce4

Observation 5ae39b50-5c3c-432d-bd8c-bee203ea17d7 · outbound

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

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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source=arxiv_source observed=2026-08-03T04:29:01.969144Z digest=sha256:17b2d8334ea73c451c6556ba51f930da46ca8825e032cc2687c4455e593f56a7

Observation f8e385b8-80ac-4237-b3e7-aee99fe7ab21 · outbound

This paper cites Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

Reference 12

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source=arxiv_source observed=2026-08-03T04:29:02.043415Z digest=sha256:aae99855cf27411a13170b603b97fe9fc03cb575353aab7696a2647107725924

Observation 93350a4b-e514-4c34-a675-ef316686ae6d · outbound

This paper cites European conference on computer vision , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation European conference on computer vision , pages=

Reference 13

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source=arxiv_source observed=2026-08-03T04:29:02.144814Z digest=sha256:0e6a6eb66162fae2b42cfb58f1efcaa763987d63075685c368cd91aa5fe05312

Observation fdadce94-9bdb-4d93-b27c-95926ffdfb73 · outbound

This paper cites IEEE Journal of Biomedical and Health Informatics , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation IEEE Journal of Biomedical and Health Informatics , volume=

Reference 14

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source=arxiv_source observed=2026-08-03T04:29:02.276812Z digest=sha256:09f8ca7cd7bbf9e4fc8ff0a3659b5cb65abb3d8b526471ab6d17290f18641e6f

Observation 60d43a42-1956-4c91-add7-41e6ca7ba23c · outbound

This paper cites The British journal of radiology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation The British journal of radiology , volume=

Reference 15

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source=arxiv_source observed=2026-08-03T04:29:02.439577Z digest=sha256:fd76fad9a7c4d34a7264efa282bec27c86f6d662ea62c811584c2751adabf852

Observation fcc4957d-d822-46df-89c3-9e6d82c56a48 · outbound

This paper cites Abdominal Radiology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Abdominal Radiology , volume=

Reference 16

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source=arxiv_source observed=2026-08-03T04:29:02.598927Z digest=sha256:1bce41498fb3997a9e1986aa2c668bf44e5562e5728afaa28a7325f09407d4e9

Observation b1426eab-d3d7-44f4-9368-76064c223db7 · outbound

This paper cites CA: a cancer journal for clinicians , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation CA: a cancer journal for clinicians , volume=

Reference 17

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source=arxiv_source observed=2026-08-03T04:29:02.721664Z digest=sha256:cc27a6216fe2bd3ce3ffb86e67bd1210747a322aba9fee2622dbc9852130d3a8

Observation d1b1a4e2-3305-4e66-a0aa-ad9ef1178d77 · outbound

This paper cites Hepatology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Hepatology , volume=

Reference 18

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source=arxiv_source observed=2026-08-03T04:29:02.845407Z digest=sha256:4096517f7af879327f759e0551f31947d32b200dee913313638fd04513d93cf1

Observation 0906bb84-6fb5-4402-9792-afcf6f424a43 · outbound

This paper cites Hepatology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Hepatology , volume=

Reference 19

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source=arxiv_source observed=2026-08-03T04:29:02.980760Z digest=sha256:82a9bedaccf1c5d51e2eb251ff31787aa9fc65d4f6f8b5e98a3da9ff2c8da44f

Observation aa50ff5b-5b05-482c-a9a3-5d1420d1cff6 · outbound

This paper cites Hepatology , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Hepatology , volume=

Reference 20

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source=arxiv_source observed=2026-08-03T04:29:03.112747Z digest=sha256:7dbb4afe6ae562172f373feb489cdb966e3ba1051cbdedc370a09d6fd2a1765a

Observation ab0e4b7b-a079-49c7-9b55-08f1c2b21ada · outbound

This paper cites Machine Vision and Applications , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Machine Vision and Applications , volume=

Reference 21

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source=arxiv_source observed=2026-08-03T04:29:03.246428Z digest=sha256:129eb617710e524f2b484387422f8087d35fdefbdf7a73190036c647b4fbfc02

Observation 24b8524b-d8c9-491a-9e89-daae0d74fad9 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 22

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source=arxiv_source observed=2026-08-03T04:29:03.376134Z digest=sha256:40944fab74b0881eb4d33b31bdbe04c0aba0a861f51d338c666884c4390a8c54

Observation 402567ad-b384-4315-b703-34a2ff7be25d · outbound

This paper cites arXiv preprint arXiv:2509.02379 , year=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation arXiv preprint arXiv:2509.02379 , year=

Reference 23

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source=arxiv_source observed=2026-08-03T04:29:03.475640Z digest=sha256:2289f99d8324b1ef538f5eea32078fc9f0bbf53f4150fa6d8063c19b448dd163

Observation 4ebf1ec2-df3a-45fe-b795-9facf275100b · outbound

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

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 24

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source=arxiv_source observed=2026-08-03T04:29:03.592760Z digest=sha256:b881e0968eb2be69d619d5b1c03de699a60f8878bb9c5d8876ccbabee4e77772

Observation 2c3fc9c1-13ad-497c-89f6-d8fb0c947c55 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 25

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source=arxiv_source observed=2026-08-03T04:29:03.638253Z digest=sha256:c2079c78c969c38af87368def11156a1ec7d0a46d58e3a10eca5698d78d5d9c3

Observation 8ac83655-f7d8-49a3-8cc2-88b29fb3de76 · outbound

This paper cites Advances in neural information processing systems , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Advances in neural information processing systems , volume=

Reference 26

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source=arxiv_source observed=2026-08-03T04:29:03.699427Z digest=sha256:24f4cc4e4d7b202623a653993c3795a1bc50eb0ad5595e30213d8d806c3dfe2d

Observation dc1129a7-065d-4512-9503-1775b892023a · outbound

This paper cites ACM Computing Surveys , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation ACM Computing Surveys , volume=

Reference 27

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source=arxiv_source observed=2026-08-03T04:29:03.751227Z digest=sha256:15ffb764906bfbbcdba4ca025d728719f87272e861bf47a0fc50bed52a6f7d97

Observation e3105c46-ca3c-4973-b4c5-742a2cd92e48 · outbound

This paper cites Scientific reports , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Scientific reports , volume=

Reference 28

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source=arxiv_source observed=2026-08-03T04:29:03.807265Z digest=sha256:8e02169df15e6177496aeddf115d453bcb5a651339055c0480dc89532a64a825

Observation cd6e09e8-2b02-450e-95ab-b0345e0904ce · outbound

This paper cites Magnetic Resonance Imaging Clinics , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Magnetic Resonance Imaging Clinics , volume=

Reference 29

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source=arxiv_source observed=2026-08-03T04:29:03.885309Z digest=sha256:72304b3f64382aa76c066ef87375d8e9059d86386574dbed95593b98516cbcbc

Observation 34011ffd-8323-4267-9b7e-941a1fb9bfdc · outbound

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

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 30

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source=arxiv_source observed=2026-08-03T04:29:03.956314Z digest=sha256:39f125b5cd9b6a2b44d03a3cc6fe0103ee7d1fcfc9d11d7b7b97bde6594d15f4

Observation 8caa9a0c-9d86-43d0-a403-822d84c68553 · outbound

This paper cites DINOv3.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation DINOv3

Reference 31

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source=arxiv_source observed=2026-08-03T04:29:04.063694Z digest=sha256:81672a7a4c427e2ca5c8d54541be719e4b5dfcbf68fc3d24863babec8c75e2f8

Observation a12d0bc2-df29-4ce0-8a91-6f4849acefbf · outbound

This paper cites Medical Image Analysis , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Medical Image Analysis , volume=

Reference 32

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source=arxiv_source observed=2026-08-03T04:29:04.135572Z digest=sha256:277d9d355e3bc798e87f68622eeaa464c281e7b9979e9ab16f60396892fc93e3

Observation a1cd6591-0e44-43cb-ab19-b8dd6b5448cc · outbound

This paper cites Advances in neural information processing systems , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Advances in neural information processing systems , volume=

Reference 33

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source=arxiv_source observed=2026-08-03T04:29:04.218439Z digest=sha256:c36147e4bedb39d77211af48043150f522b52c07ccba4658b2c0294b94e1c2b0

Observation 48840cf8-3cc6-4872-b7fa-96069d94f214 · outbound

This paper cites Image and vision computing , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Image and vision computing , volume=

Reference 34

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source=arxiv_source observed=2026-08-03T04:29:04.298612Z digest=sha256:7c5bfad8eacc2f5a6444fe3b1834daf2fbc0d64e4d400aa8b487aa67fcde8c31

Observation 7b98b80e-8872-4da1-9e2d-53288c40de0c · outbound

This paper cites IEEE transactions on medical imaging , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation IEEE transactions on medical imaging , volume=

Reference 35

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source=arxiv_source observed=2026-08-03T04:29:04.353422Z digest=sha256:0746bd54e7f04f29d42631148ace27dd3d790c1d934529e19dfa0957b7108755

Observation 2fce5d83-d43f-4af9-b8e8-21267891310a · outbound

This paper cites International conference on medical image computing and computer-assisted intervention , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation International conference on medical image computing and computer-assisted intervention , pages=

Reference 36

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Observation 7ed286c7-6d89-44c7-ab62-3d65001c0a56 · outbound

This paper cites International conference on medical image computing and computer-assisted intervention , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation International conference on medical image computing and computer-assisted intervention , pages=

Reference 37

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source=arxiv_source observed=2026-08-03T04:29:04.501744Z digest=sha256:56324a77e34464e2e10e0e51f42f2ad4750eb101ddb980fc6975a3c3c0f7064f

Observation 678cdfd8-192a-4f01-9f3f-03433a172ded · outbound

This paper cites Computer Methods and Programs in Biomedicine , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Computer Methods and Programs in Biomedicine , volume=

Reference 38

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unresolved
no resolver link, observed 2026-08-03T04:29:04.548672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:29:04.548672Z digest=sha256:aad4dedf71819b68a734e59320b22141cbef68cd7b984e01a8b726595548644a

Observation 9668cac3-ed88-4d01-a5e8-20bbf0ac7fc3 · outbound

This paper cites IEEE transactions on medical imaging , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation IEEE transactions on medical imaging , volume=

Reference 39

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no resolver link, observed 2026-08-03T04:29:04.621377Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.621377Z digest=sha256:95adb47b232aa39b16b21f5bd29b2b78bc74069c09ff42b5b77cee280266606b

Observation 7c3584ae-1585-46b4-bb78-356018c01a17 · outbound

This paper cites International conference on medical image computing and computer-assisted intervention , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation International conference on medical image computing and computer-assisted intervention , pages=

Reference 40

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no resolver link, observed 2026-08-03T04:29:04.704776Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.704776Z digest=sha256:7fbe54302c8558760534a7d95098552b9e6cc02263b03c215ddc470febc2006e

Observation 830f7007-498f-4fbc-bddf-772bcd4dad70 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 41

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no resolver link, observed 2026-08-03T04:29:04.761272Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.761272Z digest=sha256:0c387790ec5eb529d6d1a34d21d2cdd3123ae221b61bf2a161e47300897f5e76

Observation 74a441c9-3d5d-45bb-a0a5-1bca23974db3 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 42

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no resolver link, observed 2026-08-03T04:29:04.843353Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.843353Z digest=sha256:17c2519f1db6f305366ec065c57f1be087f2c3f92d6b4d915cea5e6ebc9644c8

Observation 3ceb806e-198e-4334-9c63-8ca1375679a8 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation IEEE Transactions on Geoscience and Remote Sensing , volume=

Reference 43

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unresolved
no resolver link, observed 2026-08-03T04:29:04.913434Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.913434Z digest=sha256:d6b9b4caae99259827d2715606b0ba3e407b4afb4230e98d8c697456451be198

Observation f591dbfa-b408-45b1-9522-2c99d4c01c7b · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 44

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no resolver link, observed 2026-08-03T04:29:04.954667Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:04.954667Z digest=sha256:2aeed1a3259f5ca957784df870f0ed5c55eab0a8c38ad4b3774340c4d93ba43f

Observation c6ff0887-b62a-463a-a4cd-b38810136768 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 45

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no resolver link, observed 2026-08-03T04:29:05.026143Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.026143Z digest=sha256:4d326c12bc5339eff672e8aeac7cb39fb36608cecd9d1b37b683b4fe97572783

Observation fcf8edae-7f06-4671-ae8a-10f88f26efa6 · outbound

This paper cites Medical image analysis , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Medical image analysis , volume=

Reference 46

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no resolver link, observed 2026-08-03T04:29:05.131425Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.131425Z digest=sha256:5628ec3ba3fac27a0a6c715dff87a68ca91c1a2f8f127278ec8040427ed3a80b

Observation 8c699eb8-4f45-422b-acc5-190d57fa634f · outbound

This paper cites 2024 , month=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation 2024 , month=

Reference 47

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verified exact
doi, observed 2026-08-03T04:34:10.975819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-03T04:29:05.190722Z digest=sha256:c285c8f9c21abda7da2ffc6612ca65b00156690bb02ffacbc52416523533511a

Observation 382665b2-d1a6-471d-9153-694ff8ef0dd4 · outbound

This paper cites Radiology: Artificial Intelligence , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Radiology: Artificial Intelligence , volume=

Reference 48

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no resolver link, observed 2026-08-03T04:29:05.270525Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.270525Z digest=sha256:3024a09aa67a07c4591b4f3f7129ee023af68051e05340562ca864e99e6e74b4

Observation 7049f98f-e6a7-47f3-a758-4f5db28fdd62 · outbound

This paper cites Vision interface , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Vision interface , volume=

Reference 49

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no resolver link, observed 2026-08-03T04:29:05.322194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:29:05.322194Z digest=sha256:33f59c452ddc47655f12292b5e813daa989d268c8d6b4bdc7708e40e8b97b2e0

Observation baae3112-18bd-43a4-b85f-613f02a8d657 · outbound

This paper cites Optics letters , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Optics letters , volume=

Reference 50

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no resolver link, observed 2026-08-03T04:29:05.407635Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.407635Z digest=sha256:a9c0b43fb2d74052f09bae8b2f65186d2efd2b60192ca388e89ac7bde0705d91

Observation a8006dcb-636f-4e33-8030-f20296284fd7 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 51

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no resolver link, observed 2026-08-03T04:29:05.460189Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.460189Z digest=sha256:c9b33612a4c5589d3ae04898028b8abdb13231d0b04bdcf7052879eb69d83b9f

Observation 99ba161a-a10e-4cf1-8576-6519a5020b87 · outbound

This paper cites BMC medical imaging , volume=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation BMC medical imaging , volume=

Reference 52

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no resolver link, observed 2026-08-03T04:29:05.537769Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.537769Z digest=sha256:efae0fa3a6d1daa54a23b2c40ae5caa2eaac88cc6bc910b78d6b60d48399239c

Observation 88f31dc4-d824-4e76-ae09-10c2d0136f05 · outbound

This paper cites Decoupled Weight Decay Regularization.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Decoupled Weight Decay Regularization

Reference 53

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no resolver link, observed 2026-08-03T04:29:05.619758Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.619758Z digest=sha256:86b3d6841da9498e72691d65ea6078be89d6080157c89e335b7998c7baa50244

Observation db7f5246-f88d-42a7-82a3-d507ea76a16b · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation International Conference on Learning Representations (ICLR) , year=

Reference 54

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no resolver link, observed 2026-08-03T04:29:05.677228Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.677228Z digest=sha256:47fdaccd01d7f5dbc3d2178e7796fb56d09c87c46dcdd1087cb81843303e593d

Observation e4396347-4132-4366-9a51-22c3edc98b2d · outbound

This paper cites Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 55

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no resolver link, observed 2026-08-03T04:29:05.752620Z

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source=arxiv_source observed=2026-08-03T04:29:05.752620Z digest=sha256:79b8236b9f9d677daae3015878f30f857bd373444a6117c297bc27ef5f5ddb3d

Pith citing papers

No inbound Pith citation observations are available.