Paper Citation Record · LEDGER

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

As of 6 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 7 inbound Pith citation observations for arXiv:2508.20909.

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

pith.paper-citation-record.v1
2508.20909 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T20:22:41.555806Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:57:48.304201Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-04T07:59:40.044548Z

Reference resolution

35 of 35 outbound references displayed

verified exact10
verified fuzzy24
unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c405123b-64b3-4c62-8a62-471eea62b6fc · outbound

This paper cites Artificial intelligence–enabled rapid diagnosis of patients with covid-19.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Artificial intelligence–enabled rapid diagnosis of patients with covid-19

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.110775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d26378e9-57ae-4ae5-8da6-983780c72c0a · outbound

This paper cites Unetr++: delving into efficient and accurate 3d medical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Unetr++: delving into efficient and accurate 3d medical image segmentation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.087795Z

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Observation f8b9d974-cfab-4eac-acb3-d77788dd1a62 · outbound

This paper cites nn- former: V olumetric medical image segmentation via a 3d transformer.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation nn- former: V olumetric medical image segmentation via a 3d transformer

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.101616Z

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Observation b8cb8a21-a028-43a6-ac8f-4e6bb7c2d2cb · outbound

This paper cites Transmed: Transformers advance multi- modal medical image classification.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Transmed: Transformers advance multi- modal medical image classification

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.091798Z

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Observation 3d1f260d-b160-4f90-92bf-5bb9e729521a · outbound

This paper cites WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.720022Z

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Observation bbb16afb-1211-4457-baf6-ff3f91fb8cf6 · outbound

This paper cites An anatomy-aware frame- work for automatic segmentation of parotid tumor from multimodal mri.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation An anatomy-aware frame- work for automatic segmentation of parotid tumor from multimodal mri

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.106335Z

Source-reported events for the cited work

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Observation 124bdc7c-71c1-4d01-a3a9-e0b2a1483bde · outbound

This paper cites A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.714116Z

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Observation b714a38b-772a-462c-a24a-4389684204bb · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.097828Z

Source-reported events for the cited work

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Observation db39f6d2-42a8-4e10-802f-6c5058c1bd85 · outbound

This paper cites Review of semantic segmentation of medical images using modified architectures of unet.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Review of semantic segmentation of medical images using modified architectures of unet

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.114782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c2af2372-b459-467c-a295-1dfcc9b22c01 · outbound

This paper cites Segment anything.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Segment anything

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.065297Z

Source-reported events for the cited work

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Observation f7ed2099-2c73-42b1-a4ad-8c34ff40f715 · outbound

This paper cites Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.112100Z

Source-reported events for the cited work

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Observation b93cc12b-a0d8-4e9b-a463-19489f9540f1 · outbound

This paper cites Segment anything model for medical image segmentation: Current applications and future directions.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Segment anything model for medical image segmentation: Current applications and future directions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.015374Z

Source-reported events for the cited work

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Observation 7bfc84fe-e01a-47dd-b249-61af4f5b3f8c · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Medical sam adapter: Adapting segment anything model for medical image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.108295Z

Source-reported events for the cited work

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Observation 94dabdc1-0460-4ba2-bbe2-20924fe1ec73 · outbound

This paper cites SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.691221Z

Source-reported events for the cited work

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Observation b4bd4774-7f68-412b-a43e-11b8ae55a6a9 · outbound

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

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:22:50.702687Z

Source-reported events for the cited work

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Observation 370fa0db-f45b-47ec-a1bf-8106d6fd89f3 · outbound

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

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:22:50.679850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 18397311-5588-4a30-bede-3eb00d3eb645 · outbound

This paper cites DINOv3.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation DINOv3

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:22:50.696311Z

Source-reported events for the cited work

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Observation f4dd1da2-524e-4cbf-b3ac-50440e8dc630 · outbound

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

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation SAM 2: Segment Anything in Images and Videos

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:22:50.685191Z

Source-reported events for the cited work

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Observation 729e5a5b-d66a-40a3-846c-60d66026673c · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.099589Z

Source-reported events for the cited work

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Observation 7d1ef1e0-80bc-48bb-9109-392bdeb00b58 · outbound

This paper cites 3d mri brain tumor segmentation using autoencoder reg- ularization.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation 3d mri brain tumor segmentation using autoencoder reg- ularization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.069478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52808d34-d683-4715-8fc8-d605294823f5 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.023048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation edcea13c-8cda-4e8e-9e14-e782798a271b · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:22:50.684935Z

Source-reported events for the cited work

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Observation f588dccd-8bd5-4bd2-b533-c37013550e45 · outbound

This paper cites U-kan makes strong backbone for medical image segmentation and generation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation U-kan makes strong backbone for medical image segmentation and generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.028929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 531123b1-4a54-4195-b58c-8a75883c8a51 · outbound

This paper cites Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.707712Z

Source-reported events for the cited work

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Observation 3653539c-6c87-4da1-b184-6a04836f4243 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Kvasir-seg: A segmented polyp dataset

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.075065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d182dea-6740-4825-8849-3b92d8d39ee2 · outbound

This paper cites an unresolved cited work.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-18T20:22:51.079445Z

Source-reported events for the cited work

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Observation 10e8cb7f-9460-4fc3-a6f2-1842def81551 · outbound

This paper cites Drishti-gs: Retinal image dataset for optic nerve head(onh) segmentation.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Drishti-gs: Retinal image dataset for optic nerve head(onh) segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.036986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf0ec99b-1a3a-475a-bd0d-b955e7b345c6 · outbound

This paper cites Dataset of breast ultrasound images.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Dataset of breast ultrasound images

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.104097Z

Source-reported events for the cited work

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Observation 543e1a7d-a0ab-4ff5-8a00-6e669e3f5c2f · outbound

This paper cites Cellbindb: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Cellbindb: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.045861Z

Source-reported events for the cited work

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Observation 7b933cc0-8e92-4baa-9ea6-bc2a00f2bcee · outbound

This paper cites Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.037784Z

Source-reported events for the cited work

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Observation b19fd796-df0d-4897-906e-21f10f0c5c4a · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source images.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Multivariate mixture model for myocardial segmentation combining multi-source images

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.049545Z

Source-reported events for the cited work

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Observation 048299f8-1205-48e1-8565-9accb4682040 · outbound

This paper cites Myops-net: Myocardial pathology segmentation with flexible combi- nation of multi-sequence cmr images.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Myops-net: Myocardial pathology segmentation with flexible combi- nation of multi-sequence cmr images

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.116171Z

Source-reported events for the cited work

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Observation 96b3c573-d92d-4044-adb6-2974078f1cf5 · outbound

This paper cites Prostatex zone segmentations [data set].

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Prostatex zone segmentations [data set]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.053308Z

Source-reported events for the cited work

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Observation 5c157bdd-6b49-47d1-a839-c534eb2f6f17 · outbound

This paper cites m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.673017Z

Source-reported events for the cited work

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Observation 40e0bc71-3e5a-4a7c-871d-3c8d4e3e7425 · outbound

This paper cites Swin-umamba: Mamba-based unet with imagenet-based pretraining.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation Swin-umamba: Mamba-based unet with imagenet-based pretraining

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:22:51.095098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation b93cf6d7-7df5-48ab-8cf1-a3bb5f5cf287 · inbound

Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics cites this paper.

Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T10:57:48.304201Z

Source-reported events for the cited work

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Observation ce01ad75-147d-47a9-b422-9c58bbb5ada2 · inbound

Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding cites this paper.

Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T15:16:07.463915Z

Source-reported events for the cited work

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Observation cff98fa8-b1cf-4e30-aba1-4fae60d0a710 · inbound

HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios cites this paper.

HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-02T07:46:45.603284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 21cebcc6-e7c5-4b67-b652-a08e830ac43c · inbound

SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation cites this paper.

SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T20:48:55.568173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a432f727-0799-4839-a62a-d284b0aad4bd · inbound

DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation cites this paper.

DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T00:19:13.020460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation cites this paper.

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

Resolution: metadata mismatch; 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 2026-08-06T06:34:29.942622+00:00.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:03.592760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.