Paper Citation Record · LEDGER

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.12016.

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

pith.paper-citation-record.v1
2501.12016 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:39:13.331302Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

37 of 37 outbound references displayed

verified exact0
verified fuzzy35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53febafe-ebbe-4f63-bd0d-9e296e291865 · outbound

This paper cites RETFound.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? RETFound

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.898096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0c49c52-dbf4-4d9e-8c77-222685fb0c12 · outbound

This paper cites an unresolved cited work.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:39:13.880716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a3c07586-5801-4b05-8e89-34372b06784f · outbound

This paper cites A visual-language foundation model for computational pathology.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A visual-language foundation model for computational pathology

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.806766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79e58375-a092-48b8-846f-de9c4cedc900 · outbound

This paper cites For the five-class DR detection, we first calculated the class-specific AUC and maximum F1 score, followed by macro-average AUC and macro-average maximum F1 score.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? For the five-class DR detection, we first calculated the class-specific AUC and maximum F1 score, followed by macro-average AUC and macro-average maximum F1 score

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.866921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac8e1306-8309-4c0e-9e18-39ab6fad08ae · outbound

This paper cites The CIEMS consisted of Indian participants aged 30-100 years.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? The CIEMS consisted of Indian participants aged 30-100 years

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.852259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2122af67-1b2a-4a49-a445-54e7f4712ed7 · outbound

This paper cites *=applies to ResNet50 model only.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? *=applies to ResNet50 model only

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.821107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3758896c-a182-4b99-9c02-544b7f4956f1 · outbound

This paper cites Foundation models in ophthalmology.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Foundation models in ophthalmology

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.749603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 61384eb6-c4ea-4666-9aa7-de9b4f5a5671 · outbound

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

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A foundation model for generalizable disease detection from retinal images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.791278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c01de817-b487-4c77-9b43-ff01a660e179 · outbound

This paper cites Development and Validation of a Multimodal Multitask Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Development and Validation of a Multimodal Multitask Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.777028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 94e0893e-88f6-417f-9abd-87ae24acb37c · outbound

This paper cites On the Opportunities and Risks of Foundation Models2021.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? On the Opportunities and Risks of Foundation Models2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.763300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6cdb251a-5f55-481e-91e7-92c9eda04bdb · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Swin transformer v2: Scaling up capacity and resolution

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.693862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fbf4395-649b-4f7c-a882-226f465ecc03 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? An empirical study of training self-supervised vision transformers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.735998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6b3f5226-1137-45ad-954d-2efafdac8e94 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Masked autoencoders are scalable vision learners

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.722436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7ee076f5-9203-4209-91da-c5484c33e201 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Deep Residual Learning for Image Recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.708481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6b98d76b-a935-401f-938b-e6ad456d62de · outbound

This paper cites When do we not need larger vision models? European Conference on Computer Vision; 2025: Springer.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? When do we not need larger vision models? European Conference on Computer Vision; 2025: Springer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.648955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0bbea212-8ebc-4121-bccd-4f6b03f51a1d · outbound

This paper cites Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.678300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 827bf889-2243-41a9-8855-81112e0fd9b5 · outbound

This paper cites A survey on deep learning in medical image analysis.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A survey on deep learning in medical image analysis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.663613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5fab6d45-b09c-4f6f-bd77-9296d6b440db · outbound

This paper cites These comparisons were conducted across various downstream ocular and systemic disease detection tasks, using varying fine-tuning sample sizes and multiple external test sets.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? These comparisons were conducted across various downstream ocular and systemic disease detection tasks, using varying fine-tuning sample sizes and multiple external test sets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.837058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 52db96d6-9555-41b5-878f-122c21261292 · outbound

This paper cites Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.634210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 578e4dd3-6ae0-4926-8798-8a45499c8e03 · outbound

This paper cites Cohort Profile: The Singapore Epidemiology of Eye Diseases study (SEED).

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Cohort Profile: The Singapore Epidemiology of Eye Diseases study (SEED)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.620034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e3cbd5a-28a6-4291-9cc9-174e47316726 · outbound

This paper cites Refractive error in central India: the Central India Eye and Medical Study.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Refractive error in central India: the Central India Eye and Medical Study

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.606589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f85cec33-496d-4959-9caf-999f573fdd28 · outbound

This paper cites The Beijing Eye Study.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? The Beijing Eye Study

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.592117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dca6b36c-a0c6-4fe9-9c71-0ab5f2e3233f · outbound

This paper cites OCT Angiography Metrics Predict Progression of Diabetic Retinopathy and Development of Diabetic Macular Edema: A Prospective Study.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? OCT Angiography Metrics Predict Progression of Diabetic Retinopathy and Development of Diabetic Macular Edema: A Prospective Study

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.578349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b7c13499-3e8c-413a-831b-092eb7e7904b · outbound

This paper cites APTOS 2019 Blindness Detection.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? APTOS 2019 Blindness Detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.563829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8d179c6a-deba-4bb6-8c7d-8c2ca87707a9 · outbound

This paper cites FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.550198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d33192f1-9dd3-498b-8cc9-3771b100f67a · outbound

This paper cites PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.536594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c9e419b-378e-41ba-bcf3-efc03735c22f · outbound

This paper cites GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? GAMMA challenge: Glaucoma grAding from Multi-Modality imAges

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.522504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 28fd4e36-175e-458b-8611-028f365aaa2a · outbound

This paper cites Cohort Profile: The Singapore Multi-Ethnic Cohort (MEC) study.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Cohort Profile: The Singapore Multi-Ethnic Cohort (MEC) study

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.507882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16d821ca-9572-4012-84f3-5d4f30095475 · outbound

This paper cites an unresolved cited work.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:39:13.493256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd07f0c5-f845-435c-8f70-2536cf01c69c · outbound

This paper cites Cohort profile: design and methods in the eye and vision consortium of UK Biobank.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Cohort profile: design and methods in the eye and vision consortium of UK Biobank

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.478890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 35c7a7bf-d4d2-4b70-bab7-2fdd65d85b42 · outbound

This paper cites A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A method of comparing the areas under receiver operating characteristic curves derived from the same cases

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.464720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 85f0827d-1f4f-4d73-9751-7e1e7995d5f9 · outbound

This paper cites Insights into Systemic Disease through Retinal Imaging-Based Oculomics.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Insights into Systemic Disease through Retinal Imaging-Based Oculomics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.448958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a199e69-787e-4fd5-9e7d-95670fc81aab · outbound

This paper cites A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.432906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aaa38e58-8748-4325-b035-d5a9e136179a · outbound

This paper cites Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.417840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41140325-c59f-42b2-aefe-d7c15c23c121 · outbound

This paper cites Evaluating a Foundation Artificial Intelligence Model for Glaucoma Detection Using Color Fundus Photographs.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Evaluating a Foundation Artificial Intelligence Model for Glaucoma Detection Using Color Fundus Photographs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.402106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8f5ea5f-2d83-4679-80fb-57ef2fc044c9 · outbound

This paper cites RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.386925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 50c8aed5-c190-48fe-bad6-572de36164b9 · outbound

This paper cites A New Foundation Model for Multimodal Ophthalmic Images: Advancing Disease Detection and Prediction.

Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? A New Foundation Model for Multimodal Ophthalmic Images: Advancing Disease Detection and Prediction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:39:13.370565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.