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Paper Citation Record · LEDGER

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers

As of 17 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2504.15928.

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

pith.paper-citation-record.v1
2504.15928 v2

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:57.315168Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

64 of 64 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 31d01a3e-8e23-4014-a07b-f21b665a37db · outbound

This paper cites an unresolved cited work.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Unresolved cited work

Reference 1

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Observation 0d118200-a605-4b03-b329-42f4a68229f4 · outbound

This paper cites The lancet global health commission on global eye health: vision beyond 2020,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers The lancet global health commission on global eye health: vision beyond 2020,

Reference 2

Resolution
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Observation 6bae723f-2f7e-4a76-874d-9618a9363e17 · outbound

This paper cites Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study,

Reference 3

Resolution
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Observation cba47874-438c-4d95-8439-d4e20c1629b1 · outbound

This paper cites A generalist vision–language foundation model for diverse biomedical tasks,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A generalist vision–language foundation model for diverse biomedical tasks,

Reference 4

Resolution
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Observation 1ce81585-a6fd-45b4-bdc0-7b4f6f7e1462 · outbound

This paper cites A deep learning system for detecting diabetic retinopathy across the disease spectrum,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A deep learning system for detecting diabetic retinopathy across the disease spectrum,

Reference 5

Resolution
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Observation 78ca36b8-2eef-4a0f-9c9f-24fcbd689091 · outbound

This paper cites Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy,

Reference 6

Resolution
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Source-reported events for the cited work

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Observation efc72dd0-7d60-4b46-80c1-dac4bac3ed41 · outbound

This paper cites A deep network deepopacitynet for detection of cataracts from color fundus photographs,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A deep network deepopacitynet for detection of cataracts from color fundus photographs,

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 4bb9867b-5109-45ae-a849-5096bb7bbce0 · outbound

This paper cites A foundation model for generalizable disease detection from retinal images,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A foundation model for generalizable disease detection from retinal images,

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 82f8ca3c-5237-44d5-b648-0382310fc7c0 · outbound

This paper cites VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence

Reference 9

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Source-reported events for the cited work

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Observation f0a5bd23-604f-4ed6-aac3-250ff37f846c · outbound

This paper cites A guide to deep learning in healthcare,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A guide to deep learning in healthcare,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 4a9e1481-20d4-45a6-b9de-c0d48ce0cb3a · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers On the Opportunities and Risks of Foundation Models

Reference 11

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Source-reported events for the cited work

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Observation 50f1fe9f-09e8-4801-b905-91fb6fb4bec9 · outbound

This paper cites On the opportunities and risks of foundation models for natural language processing in radiology,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers On the opportunities and risks of foundation models for natural language processing in radiology,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 13d6ab5b-15ac-45c5-a8e8-2246a94306a2 · outbound

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

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers DINOv2: Learning Robust Visual Features without Supervision

Reference 13

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Source-reported events for the cited work

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Observation eb51f927-bca8-4576-9caf-cbf58021d051 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Learning transferable visual models from natural language supervision,

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 41afe729-c102-445b-9b6d-4c53d017fee5 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 15

Resolution
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Source-reported events for the cited work

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Observation 53add342-41b8-4fdf-bf38-97778866367a · outbound

This paper cites Query rewriting in retrieval-augmented large language models,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Query rewriting in retrieval-augmented large language models,

Reference 16

Resolution
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Source-reported events for the cited work

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Observation b03d3566-6cae-44d7-8405-85ba38268ba4 · outbound

This paper cites an unresolved cited work.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 15677ce9-609d-4e92-a473-c6332eddf4cc · outbound

This paper cites User acceptance of information technology: Toward a unified view,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers User acceptance of information technology: Toward a unified view,

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 39248266-4d76-4dde-9ade-45392ea16ead · outbound

This paper cites Visualization of supervised and self-supervised neural networks via attribution guided factorization,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Visualization of supervised and self-supervised neural networks via attribution guided factorization,

Reference 19

Resolution
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Source-reported events for the cited work

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Observation 6b9cdb65-6c38-4a86-9715-a998ac6ff8c0 · outbound

This paper cites Code-free deep learning glaucoma detection on color fundus images,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Code-free deep learning glaucoma detection on color fundus images,

Reference 20

Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9b257c2d-8fbf-4c8c-ab1c-0b671223782f · outbound

This paper cites Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study,

Reference 21

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Source-reported events for the cited work

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Observation 54ca56de-d80d-4bf0-8abb-cf3afb59e13b · outbound

This paper cites Bisonget al., Building machine learning and deep learning models on Google cloud platform.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Bisonget al., Building machine learning and deep learning models on Google cloud platform

Reference 22

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 3e1448c7-7425-40b8-96e7-29d71737a4af · outbound

This paper cites Barnes,Microsoft Azure essentials Azure machine learning.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Barnes,Microsoft Azure essentials Azure machine learning

Reference 23

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation dc947805-9a14-4677-9fdf-e34b1bec866d · outbound

This paper cites Towards a general-purpose foundation model for computational pathology,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Towards a general-purpose foundation model for computational pathology,

Reference 24

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b8a58650-208c-4c93-81b5-e1efefba8339 · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers A visual–language foundation model for pathology image analysis using medical twitter,

Reference 25

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e054e2af-ea10-4453-beb3-c2a11b86b2ab · outbound

This paper cites Uncertainty-inspired open set learning for retinal anomaly identification,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Uncertainty-inspired open set learning for retinal anomaly identification,

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 889edc9a-49fc-4fd5-b6c4-7eec1edecad6 · outbound

This paper cites Enhancing ai reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Enhancing ai reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis,

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 35f06673-4ff3-45ab-8773-9407b17291d0 · outbound

This paper cites Deep triplet hashing network for case-based medical image retrieval,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Deep triplet hashing network for case-based medical image retrieval,

Reference 28

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 54bf6c0d-443b-4e05-9946-39cdbb9977dc · outbound

This paper cites Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs,

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation dc31bf5c-e1b5-4474-9cce-eae51bc66859 · outbound

This paper cites Zero-shot text-to-image generation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Zero-shot text-to-image generation,

Reference 30

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2eb192c5-b999-4e6b-841e-0031cc0a47de · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 31

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5bcebd87-1e2a-4eb7-93cc-e2a223a32d8f · outbound

This paper cites Improving image generation with better captions,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Improving image generation with better captions,

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b585e8c1-0c60-42b0-a64e-47903d374559 · outbound

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

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation

Reference 33

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d7ea0773-fd81-4993-a1e2-523c5d3ed75e · outbound

This paper cites Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model

Reference 34

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:17:57.076181Z digest=sha256:c78213cae47a379807eeba5b705c1a2dee509730a6c4f0dc5f54b0dfe10d8e94

Observation 8cb1216a-0da4-4d44-bbb3-51a936752886 · outbound

This paper cites Cohort profile: the singapore epidemiology of eye diseases study (seed),.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Cohort profile: the singapore epidemiology of eye diseases study (seed),

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.782560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.080834Z digest=sha256:0fd72a456dc2e170c370aa44589d503d5234247c8b6c9b539453488281e00710

Observation e500eb74-611c-4caa-be27-c890532a5b16 · outbound

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

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-16T11:17:57.084598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:17:57.084598Z digest=sha256:a2b0c4ec3852c4e03c176bd56aa590fd7880fd5789d39712133adf73d33c0e57

Observation e45f94b8-11d3-4a4a-871d-54bbb88884a7 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Lora: Low-rank adaptation of large language models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T11:17:57.196197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:17:57.196197Z digest=sha256:a476e8c2f8da5a6650cfc7161939b2d34a9b60b6c4b8003e7604765c49bc5ecc

Observation 9bed1cf4-31c9-42b3-9f1f-185af64a3f9d · outbound

This paper cites Publicly Available Clinical BERT Embeddings.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Publicly Available Clinical BERT Embeddings

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T11:17:57.201667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:17:57.201667Z digest=sha256:07763c4523a52dfbaadb28a7f6c5ef5a977de24ea6896469f29e096a1d44a4c3

Observation c4c3d702-e0fb-451f-b8d7-d80a12e7a428 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers What uncertainties do we need in bayesian deep learning for computer vision?

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.762660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.206261Z digest=sha256:d262c025977ad99758b62caa1c51c4ff868121f9163155cfe01618538dbb2af4

Observation c14f360e-225c-4519-994a-5c0627bd61ee · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.751460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.210054Z digest=sha256:d7a9a9572647cd44ab683a8107c7902041b56176a7f16a678bc7b07dba511a9f

Observation bde10eda-e842-4842-b479-5cde2eaa79a6 · outbound

This paper cites Youden index and associated cut-points for three ordinal diagnostic groups,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Youden index and associated cut-points for three ordinal diagnostic groups,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.740809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.213917Z digest=sha256:7e595c00aaeb9df707e0cfd98471fd42af1bd106b6b2b78fe64b4c4114ba7923

Observation 64e54255-0935-4f62-952d-4bbca9493b93 · outbound

This paper cites Determining what individual sus scores mean: Adding an adjective rating scale,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Determining what individual sus scores mean: Adding an adjective rating scale,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.729709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.217560Z digest=sha256:7c87054fcc9e66953d4615d4a8d04dc8121160ce0f5e6bca8f8e6650e0e9607b

Observation 08b30b7e-3adb-45c6-837b-6073b8b892f6 · outbound

This paper cites Cnns for automatic glaucoma assessment using fundus images: an extensive validation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Cnns for automatic glaucoma assessment using fundus images: an extensive validation,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.718767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.221770Z digest=sha256:ae243a49daa0a6ec6642f621b565029033373aa5b057513940226890c6bf5eb5

Observation f871f131-bcc8-4462-89c7-7bfef1be86b7 · outbound

This paper cites Deep learning-based glaucoma detection with cropped optic cup and disc and blood vessel segmentation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Deep learning-based glaucoma detection with cropped optic cup and disc and blood vessel segmentation,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.706670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.225467Z digest=sha256:4a2437cfa929f7bb8276b1a5d9c720c45f3b489b60060e067555c74f1ef00e2c

Observation aa618fcb-dd99-4b7c-9034-1bd2e2eab3fb · outbound

This paper cites Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.695120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.230141Z digest=sha256:cff0ad8441832f7046ece960dc8862429d14a483df519beb42b80b8b9ac5ee1f

Observation e71d5523-7d50-43b6-92a0-dcf29120b0d0 · outbound

This paper cites Advancing bag-of-visual-words representations for lesion classification in retinal images,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Advancing bag-of-visual-words representations for lesion classification in retinal images,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.684719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.234596Z digest=sha256:ccf35630163668f8849c11f3c994d53c2d16ca88b256aa0d16a1649b8232bc6f

Observation 9a84b146-97ee-40a9-8280-2d511d440e02 · outbound

This paper cites Teleophta: Machine learning and image processing methods for teleophthalmology,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Teleophta: Machine learning and image processing methods for teleophthalmology,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.673534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.239039Z digest=sha256:49a96d357fbe77d198b68550e99a87a407fe2adfb30d8d1f891c7d9cbc69e618

Observation 7f549254-b1d6-4348-b94d-c8ab5f305b3f · outbound

This paper cites Airogs: artificial intelligence for robust glaucoma screening challenge,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Airogs: artificial intelligence for robust glaucoma screening challenge,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.661154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.243285Z digest=sha256:a855da98948f1ecbb773bd1732fd0fc49a205a7f751bf1ed00256f09a99c3458

Observation 6574dd6f-6ef2-418c-874a-cb901cd0bc2e · outbound

This paper cites Deepopht: medical report generation for retinal images via deep models and visual explanation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Deepopht: medical report generation for retinal images via deep models and visual explanation,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.649776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.247025Z digest=sha256:639e4621d4e1c2c088da2a38caff8f81064692747931d0bd5de66c9663660efe

Observation fabacc81-cd3f-4a01-bde6-f2ff2413cc84 · outbound

This paper cites Fives: A fundus image dataset for artificial intelligence based vessel segmentation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Fives: A fundus image dataset for artificial intelligence based vessel segmentation,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.637960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.251206Z digest=sha256:385024c54b6ccae6d7dd1b3c9bc7b10e60071d34d58342191cdba47b0b419ee7

Observation 62ed29f7-b8a8-4cea-8ac3-0a95ce195a05 · outbound

This paper cites G1020: A benchmark retinal fundus image dataset for computer-aided glaucoma detection,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers G1020: A benchmark retinal fundus image dataset for computer-aided glaucoma detection,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.624026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.254680Z digest=sha256:5c8bdc9a5c45a0753cad7e705e22d48366ea92f8f5642ff90419b7a6b0194976

Observation 806b0a44-e43f-4a10-84ca-c4871d291fc4 · outbound

This paper cites Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.611944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.258384Z digest=sha256:250e5c0280ac3eaadd7138eba72e38097f169d9e64b98cef9a42d5e50430e656

Observation 034325a1-c68e-41ec-b7f4-8d3461fcb4b9 · outbound

This paper cites An adaptive threshold based image processing technique for improved glaucoma detection and classification,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers An adaptive threshold based image processing technique for improved glaucoma detection and classification,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.598877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.263297Z digest=sha256:a37b9ab5c65094427b2cd7e7996f45f201e1558a7a4891730a06956fa60f2cc1

Observation 6ccd35ab-a3d9-4d39-b05b-093be122b19a · outbound

This paper cites Idrid: Diabetic retinopathy–segmentation and grading challenge,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Idrid: Diabetic retinopathy–segmentation and grading challenge,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.586530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.267662Z digest=sha256:377157ef054956b79e0f2cadee0f54b08d12b99f7b139eb15f5ae7cee28a5ed7

Observation 1e7e181e-12df-4da4-b129-d34f289e171a · outbound

This paper cites Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.573162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.272720Z digest=sha256:c06934e69c73a4ae848fa75c4a4aa9d444fa39d42103a678026a8c83d47d4b47

Observation 7f33795d-b3f1-4816-bc46-808ed0fdad41 · outbound

This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.559182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.277309Z digest=sha256:aafea4896619010abce18e218ae3c6dbffd3c560e8f1cd7e087368bcd496d3ca

Observation febce1d2-b0f5-47e5-b4f1-affe03c3a76b · outbound

This paper cites Origa-light: An online retinal fundus image database for glaucoma analysis and research,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Origa-light: An online retinal fundus image database for glaucoma analysis and research,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.548574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.281198Z digest=sha256:cf501274d5ec13cc375752617c8036ccd12da02078e8fb84bbf1c65f5e0ff13b

Observation c926ff11-bb02-4db6-828d-db1ff4a70ec9 · outbound

This paper cites Dataset from fundus images for the study of diabetic retinopathy,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Dataset from fundus images for the study of diabetic retinopathy,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.535973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.285407Z digest=sha256:5e81d5abcd0fa5b50539dd25c158d8714354605cf681bbd8493b0e5f54a01c51

Observation ed1e55f3-6d70-431e-a6f2-8fff0e149b3b · outbound

This paper cites Improving medical images classi- fication with label noise using dual-uncertainty estimation,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Improving medical images classi- fication with label noise using dual-uncertainty estimation,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.522818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.293963Z digest=sha256:7144d072ac9a9f0db9ca72b9a53f2d82cd78b2181c750119d4f18e5d9c530034

Observation 37fa1ddb-52ad-4e26-a9e4-ccf25797e121 · outbound

This paper cites Auto- matic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Auto- matic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.509969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.298294Z digest=sha256:6db39b5e29894ebee3dc2eafdb59a467da4fa9a112577490137100b2a28d8873

Observation 9f9021ca-4ad9-41ad-bb2f-cf7fadc5e6e5 · outbound

This paper cites Retinal fundus multi-disease image dataset (rfmid): A dataset for multi-disease detection research,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Retinal fundus multi-disease image dataset (rfmid): A dataset for multi-disease detection research,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.498377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.302318Z digest=sha256:23b66f184e5ded0290e21726ca91bf6cfa8d6b5488c96fe1c8e973d061107827

Observation 315efc0a-77f6-431a-9dbc-555187b61ed0 · outbound

This paper cites Brset:abrazilian multilabel ophthalmological dataset of retina fundus photos,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Brset:abrazilian multilabel ophthalmological dataset of retina fundus photos,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.486594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.306370Z digest=sha256:22560e8b1f73d593b33e20c8f74714eeb39e5269852d3e5899dd7ec9e2ee38a5

Observation 013900b8-5de8-4cac-a6e6-3446965cd9b3 · outbound

This paper cites Accuracy assessment of intra- and intervisit fundus image registration for diabetic retinopathy screening,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Accuracy assessment of intra- and intervisit fundus image registration for diabetic retinopathy screening,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.473955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.310302Z digest=sha256:89098a9af1d7698fd6994312f07b89ab047be101953db5ef86a23b25e41be57c

Observation ca0c5b74-9711-4125-b70c-e591c0aca09c · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning,.

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Identifying medical diagnoses and treatable diseases by image-based deep learning,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:17:57.459447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:17:57.315168Z digest=sha256:3db29af8c68c3ed4f8271fe8062b0df3e453d96869095b7d23efc04bea3e23a5

Pith citing papers

No inbound Pith citation observations are available.