Paper Citation Record · LEDGER

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis

As of 20 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.11753.

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

pith.paper-citation-record.v1
2506.11753 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:07:13.109637Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

34 of 34 outbound references displayed

verified exact7
verified fuzzy5
unresolved21
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0638e274-e339-44e5-9cde-132bcd5124fd · outbound

This paper cites Poplin et al., ‘Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning’, Nat Biomed Eng, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Poplin et al., ‘Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning’, Nat Biomed Eng, vol

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T04:07:14.088675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 49c6d702-78b4-4561-84dd-40a18dfb3d21 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T04:07:13.956965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f80cb47-f4e1-4e9f-bbb1-30e6a2cbac1e · outbound

This paper cites Zhou et al., ‘A foundation model for generalizable disease detection from retinal images’, Nature, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Zhou et al., ‘A foundation model for generalizable disease detection from retinal images’, Nature, vol

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:10.747405Z

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Observation 86fa02dd-e646-4f61-be0e-da8815ce55ea · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 4

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Observation 128cd36a-a7cb-4816-bce4-e10a13fd2318 · outbound

This paper cites An et al., ‘Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images’, J Healthc Eng, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis An et al., ‘Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images’, J Healthc Eng, vol

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T04:07:13.806383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ccd0cd66-2849-4970-80ec-31afdc992042 · outbound

This paper cites Zhou et al., ‘AutoMorph: Automated Retinal Vascular Morphology Quan- tification Via a Deep Learning Pipeline’, Translational Vision Science & Technology, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Zhou et al., ‘AutoMorph: Automated Retinal Vascular Morphology Quan- tification Via a Deep Learning Pipeline’, Translational Vision Science & Technology, vol

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:11.037575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b4962a4-c03d-49f4-a661-0658b9df68f4 · outbound

This paper cites Staal, M.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Staal, M

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:11.110008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 380b5a8b-b6d1-4416-97ce-e35c6a247117 · outbound

This paper cites Hoover, ‘Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response’, IEEE Trans Med Imaging, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Hoover, ‘Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response’, IEEE Trans Med Imaging, vol

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:11.225349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c031a7-e6d8-4df8-96cb-ff7679d277a7 · outbound

This paper cites On Biases in a UK Biobank-based Retinal Image Classification Model.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis On Biases in a UK Biobank-based Retinal Image Classification Model

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:07:15.105969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72e589fa-862b-4016-b3a5-4f6c6c8f6cea · outbound

This paper cites Taming Transformers for High-Resolution Image Synthesis.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Taming Transformers for High-Resolution Image Synthesis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:11.354540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38c99e60-6627-4e4b-9319-b205d36cab01 · outbound

This paper cites Rombach, A.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Rombach, A

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:11.431588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 366aae74-e978-43d6-ad62-81afe382bd90 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:16.874394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb6533db-fe31-4d36-ad55-83b749cc33a4 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:16.594142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 848d589b-1035-4b06-bfee-06e6ba696d5b · outbound

This paper cites Morphology-preserving Autoregressive 3D Generative Modelling of the Brain.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:07:13.569390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3890917b-bf9d-4087-91b8-13e2107c26b0 · outbound

This paper cites When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:07:13.402284Z

Source-reported events for the cited work

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Observation f98e1bd3-6942-42c9-91f0-21c9c0768c6b · outbound

This paper cites Litrico, F.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Litrico, F

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:16.383813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de86ade1-d64c-44f5-ad5b-cfedf515f4dc · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:16.110003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b95a699f-7902-4085-8f5e-d2c4b680acfd · outbound

This paper cites Doerrich, F.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Doerrich, F

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:15.859767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 262159a1-e33e-41fa-89ea-7e1858a97a64 · outbound

This paper cites Sturm, L.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Sturm, L

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:15.740323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de7f2b1b-8bf6-4689-9bb0-38f013c22d81 · outbound

This paper cites LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.040068Z

Source-reported events for the cited work

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Observation 0944138b-b0f1-486e-afdb-7026390f0682 · outbound

This paper cites Bradbury, K.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Bradbury, K

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.120874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 575e3e93-b8c4-4c64-aeb2-d42fcf08cf57 · outbound

This paper cites Zhang, F.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Zhang, F

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.161037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7777db97-2c28-43c5-a10a-6c798a30a464 · outbound

This paper cites FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:07:14.622334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a5e617fd-420f-479f-9919-0402086eba88 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 466d17de-badf-47c5-b7b2-986e5d439e3f · outbound

This paper cites Zhang, P.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Zhang, P

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.425644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbce4e17-bf9a-4712-acd6-4c36708a73c4 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.485910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2546db02-b668-4df4-9b4c-f3160cd67594 · outbound

This paper cites Neural Discrete Representation Learning.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Neural Discrete Representation Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.537595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d88b42f6-242b-441d-bc7b-034a77b6a51c · outbound

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

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.632616Z

Source-reported events for the cited work

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Observation b71439f3-6a20-462e-bb52-291517995bd8 · outbound

This paper cites Meijering, M.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Meijering, M

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.716079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87bbb248-b195-4904-851d-51b2aa6f69df · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.802522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2229ef75-a217-4a98-a342-5223f7bfb4a5 · outbound

This paper cites Szegedy, V.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Szegedy, V

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:15.572037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1f00162-a175-47a4-8101-da573c43fe26 · outbound

This paper cites Jordon et al., ‘Synthetic Data -- what, why and how?’, May 2022.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Jordon et al., ‘Synthetic Data -- what, why and how?’, May 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:15.366418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 27fea09b-7e69-4666-8494-64fd40cd595a · outbound

This paper cites Dong et al., ‘PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers’, Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Dong et al., ‘PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers’, Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:13.109637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3edb60fb-73cf-459d-93f3-c39d215bf893 · outbound

This paper cites an unresolved cited work.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis Unresolved cited work

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:12.948836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.