Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:39:13.331302Z
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 21 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 53febafe-ebbe-4f63-bd0d-9e296e291865 · outbound
This paper cites 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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.