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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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

55 of 55 outbound references displayed

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

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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:a439cb27c8f67689bf8b71b7843f2cd292e66c2b2e817b703e489c4ebbee9c92

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:e079bddc82cb4aa64fea2237b0abec6d07848d18d5dbf921ec666b83b35da81f

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=sha256:926901e25e8fd12dc78491c49bba16a16c13a3e9d3b4b61f71c8fd5e2e16489c

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=sha256:63a35a4490932638eeacf4dd5061f94ba3d18c537fca81db435197b90f3a7c16

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=sha256:8e1ad2c60e66e5021ab9703f62fcedffa487ec228267107258ba8b7c758a545c

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=sha256:358835ee6ee4fe775f269ad3d08df69839539b84635297ee78c214229ef2f64d

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:a98a69fb55006d39ef0699f4651dca8a4164ae9b03c2d46f3428b1957c1465f6

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=sha256:0a84ac5a96be3b634f257cf44c2b23443cf52dafa2bec451d15654b004844118

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=sha256:6dc7b30d386a31366f987c1e19bc13d32770fa7d089599cc9a8d57f0f621f670

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=sha256:4eb7a94d867f150c174080bb207775004f86d911225212d9d88c7812fe3b88dc

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:d765be8a2124b2a54eeeea6cd723da2330cac6cf40ff8ed6b0d308a8759559ed

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=sha256:738e305dfce53a91fd2ea07862c5cbfb9a28618d5eb94dd0e4a9d01cd4153b6f

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=sha256:5e5763935524e0725498dae2e9d3cb4a13fceec0564fa75d47641b948a6804c6

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:ea7bd8025e0752081e1ab98f57d5de02f75ae157c5b329055de22215a0a66fea

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=sha256:84417119e5013e55ee48c62d3928a152f226b68a632b7ca6a593c04004b7bf5b

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=sha256:10b261cd84d6bc8f643bd3d7b2b6ae936c66a8a4cc92e87650f76a6ea37bcf04

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:f263de7681fc7d174ed9b3d75196a231926be052aaba4bda54843594a96847c1

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:de7696bd0f49a8f78a02ffb5f3901980c01b482325cb58f92436fc053f6cee42

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:f5b4805619bc661a116d17f4723114f9eb34d977756177737d30f909ff57e89d

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:b2041e53928683745a73f78cdf0054e8f49abc3f1a190b42ad4fddb714bbf934

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=sha256:630f9ae7cd6a2c52acdda678eae12577739f6fdf872972e32ad74ed0366d98c6

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=sha256:8c05e5266c1f145e70f462ba922580b3956a014f54cb0461d632f415a61e420e

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=sha256:516607c8a205eb7058df1d43138699e33f28c4d400d423d8e932b69958707082

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=sha256:4471b01c5d32086aa3e6665481de42871343200d01d956e9b3eea8ef40b44ee8

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:a8947dc26400d21c97ae7f73ff7861ce3e29f16f4572d73034bfdfe78f441086

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:b21e5962617c44f5932ac83b6cdf3096fac333fa01cddf105570070ef51ea26d

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=sha256:5c17ddd3ca270833fd01145e9785b9b921bf80fa4a56a90e48499aa7794cff17

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=sha256:608407890cdadb82f902379602d7b2b2bc29bb0179447a58509c61865f0368a0

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:cc98e29e8da5eea7928a3fb6c3194db8d75f18ac5e82306986028d3cbac5cb39

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=sha256:22a5ce8a9cbe285bdb8c7c5a6897fd8dc9b14aed8f2d22f32ad0cf4b4fc0eb8f

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=sha256:7c5d7a40b948dee5e92a2f6431b520c988e822a0a9b402292bf2371297dbf77a

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=sha256:09c9afb8cc5a2c6645796a018884150627f144a41316e2246f564b4e5e696de9

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=sha256:914464c0314ce16e7eacd305779e66f7c5ceb7d440e1f6bb67c546c56b8c43dc

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:da17f2ba7ecec0ae5656ed5b60bffe2702d843dfec353eaa3df4a7e2e230ba30

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:da4131c09fe62d03b8f71bef7977c8dc45a19cc0752b1d8d37a0f06ba2e116a9

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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source=arxiv_source observed=2026-08-03T04:29:04.419269Z digest=sha256:14c0d4c1c5b1f0bc8912bc1b8cbcd37a8e7e2c16e2563a51eaaca6167e19b39b

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:f965805ce06ae085f87f1cce5d2501dedba1020f62d9497e3dd17ea30be0c564

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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source=arxiv_source observed=2026-08-03T04:29:04.548672Z digest=sha256:44eaa167710bb4a66af7d6ccd84b3d4188aba43ea4f7f71db97f62f96d2828a1

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

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source=arxiv_source observed=2026-08-03T04:29:04.621377Z digest=sha256:01983daacd86e00742a7a5b98655bf6a5dec960eda901effdc8b03a7ec240952

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=sha256:8ee4cfb65eecc0baf0b752a8d12782758fdcb6d33ee5fe2eee9c2b6ae414815f

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

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source=arxiv_source observed=2026-08-03T04:29:04.761272Z digest=sha256:5f25ec16be37eb6f532ce96e24aa3aa40f2cf4b2e6a0029c00d3ee3c65ece77f

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=sha256:5638a6879aebb1927d33e68012c2f55cb162bf476df26976bda84edd948bb3ec

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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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:d145cfa03efc44fd6d81bde71f9afa871b7e284a3a6dd79f6ac6b72ecbbac89d

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=sha256:362a8e4737332d6287df1354c4a15b777185d7bbc44e9215765553c02c2af050

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

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source=arxiv_source observed=2026-08-03T04:29:05.026143Z digest=sha256:5f685d6b332cfaca3d1501d3c2bb8f50e35984aef934fc3f06223ecf6a3d5047

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

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source=arxiv_source observed=2026-08-03T04:29:05.131425Z digest=sha256:bbc90b8fd90e3d9bff4b9331daca019b7f50c600587ee6bc076afaf86710c7fa

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-03T04:29:05.190722Z digest=sha256:5139da3e64ccbfd60c76f57dba63327bd4f5c05c77e51b0f846d4b914c96a51b

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

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source=arxiv_source observed=2026-08-03T04:29:05.270525Z digest=sha256:9227cf6489effd2219bf8b40d2fd7d6f886f6be2fdecd3af6ba568a54d0d869f

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

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source=arxiv_source observed=2026-08-03T04:29:05.322194Z digest=sha256:896d337c7e3d5bece2d065a5aa94a8ec5999ac702caea2d8f181a959b9bca0c7

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

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source=arxiv_source observed=2026-08-03T04:29:05.407635Z digest=sha256:b7ea60ab0727d87d57e3ced112a9ddf4725734ae3b4def459ff32ac4796035f5

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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source=arxiv_source observed=2026-08-03T04:29:05.460189Z digest=sha256:98b7f5676138e50d06951ab88e93763595b7515143009708ae28616e1f244272

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:b5c67f70316d66475b0c183b0a78ac3b565cec043ed8f099782707b0fb474295

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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source=arxiv_source observed=2026-08-03T04:29:05.619758Z digest=sha256:9ffb95bbe698f2a0b3286c54bce20f014e2cbe700d82e7ee2f724b76b19652fd

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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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T04:29:05.677228Z digest=sha256:dd1f535a185ff7c89389c6a554b2cc24a1d560657f28fb20b30a5391fb7391e1

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=sha256:6dbf96ad284b7542050472fc0794a494a7b2967d07fe5817d5f7d9f46795de71

Pith citing papers

No inbound Pith citation observations are available.