Paper Citation Record · LEDGER

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2412.06624.

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

pith.paper-citation-record.v1
2412.06624 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:34:09.657929Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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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

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation facf4e92-2e0d-43c9-bbdf-b255b4e15623 · outbound

This paper cites Fundusq-net: A regression quality assessment deep learning algorithm for fundus images quality grading.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Fundusq-net: A regression quality assessment deep learning algorithm for fundus images quality grading

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:10.205475Z

Source-reported events for the cited work

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Observation f01d6ea6-0c62-4a5a-89cb-4f4be4c4b36e · outbound

This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 2

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Observation 6e485f6b-397a-4abe-9bde-d1769d4d708b · outbound

This paper cites Conformal prediction: A gentle introduction.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Conformal prediction: A gentle introduction

Reference 3

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Observation ce2a0a9e-f70a-48c3-928c-b243b5381d73 · outbound

This paper cites Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection

Reference 4

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Observation 0d7b7a9e-14c3-4567-a73d-4525fde960af · outbound

This paper cites u mmerle, Laura K \.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees u mmerle, Laura K \

Reference 5

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Observation da991624-9cf6-4567-93b0-1822cbf9d282 · outbound

This paper cites On the Utility of Prediction Sets in Human-AI Teams.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees On the Utility of Prediction Sets in Human-AI Teams

Reference 6

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Observation dbf828ff-644b-4b72-aaff-b7e255663cb1 · outbound

This paper cites Deep learning identifies high-quality fundus photographs and increases accuracy in automated primary open angle glaucoma detection.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Deep learning identifies high-quality fundus photographs and increases accuracy in automated primary open angle glaucoma detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:10.161364Z

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Observation e82f27e6-b361-4f21-8d21-963d3cbaf4b0 · outbound

This paper cites Automated fundus image quality assessment in retinopathy of prematurity using deep convolutional neural networks.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Automated fundus image quality assessment in retinopathy of prematurity using deep convolutional neural networks

Reference 8

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Observation 71e3a138-3e15-4fcb-b29e-10ca22895f4f · outbound

This paper cites A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

Reference 9

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Observation 8501b0c1-00f1-4dee-88cd-80c190879b0b · outbound

This paper cites Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks

Reference 10

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Observation 6d0c9a8c-dda4-422a-865e-16431d2be534 · outbound

This paper cites Adaptive conformal inference under distribution shift.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Adaptive conformal inference under distribution shift

Reference 11

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Observation 54e92a3b-cfe1-4bc7-8b60-ff549c18a34f · outbound

This paper cites Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:10.123225Z

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Observation 732493bb-5ff0-4a01-92a6-a39c145f6aec · outbound

This paper cites Deep residual learning for image recognition.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Deep residual learning for image recognition

Reference 13

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Observation 5a452c0e-b2f0-4dd2-aff6-4fabd635313e · outbound

This paper cites Sequential covariate shift detection using classifier two-sample tests.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Sequential covariate shift detection using classifier two-sample tests

Reference 14

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Source-reported events for the cited work

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Observation 394cec8d-bae7-4b89-84dc-a605bfd00e47 · outbound

This paper cites Incremental anomaly detection with guarantee in the internet of medical things.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Incremental anomaly detection with guarantee in the internet of medical things

Reference 15

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Observation 6c89d6cf-8ef5-46e4-81d5-a3e9a7b3a2a2 · outbound

This paper cites Automatic fundus image quality assessment on a continuous scale.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Automatic fundus image quality assessment on a continuous scale

Reference 16

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Observation 94856131-f3d3-4f16-8003-cd8596a7b3dc · outbound

This paper cites A deep learning ensemble method to visual acuity measurement using fundus images.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees A deep learning ensemble method to visual acuity measurement using fundus images

Reference 17

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Observation cf7a4abf-714e-4ce5-b7f7-544ae0ce4162 · outbound

This paper cites Bayesian-torch: Bayesian neural network layers for uncertainty estimation.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Bayesian-torch: Bayesian neural network layers for uncertainty estimation

Reference 18

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Observation efa46d48-04a3-4b3e-8db7-f15850a2d636 · outbound

This paper cites Leveraging uncertainty information from deep neural networks for disease detection.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Leveraging uncertainty information from deep neural networks for disease detection

Reference 19

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Observation f1f59ef4-c79d-447e-b156-901b622fe033 · outbound

This paper cites Learning deep kernels for non-parametric two-sample tests.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Learning deep kernels for non-parametric two-sample tests

Reference 20

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Observation 305cf66c-d245-406d-aa8d-b7cd39eb0d93 · outbound

This paper cites Normalized nonconformity measures for regression conformal prediction.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Normalized nonconformity measures for regression conformal prediction

Reference 21

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Observation 8acabaeb-2870-4741-9e5f-3fac2d6b7f18 · outbound

This paper cites PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

Reference 22

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Observation d04bdc96-c9b9-4600-a4d2-35b8570228be · outbound

This paper cites Pac prediction sets for meta-learning.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Pac prediction sets for meta-learning

Reference 23

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Observation 91aada22-afe4-489e-892f-04035fa893f0 · outbound

This paper cites Pac prediction sets under covariate shift.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Pac prediction sets under covariate shift

Reference 24

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Observation 76fdcd64-0815-4b5a-8e86-9b281f4fb4e7 · outbound

This paper cites Accuracy of artificial intelligence in estimating best-corrected visual acuity from fundus photographs in eyes with diabetic macular edema.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Accuracy of artificial intelligence in estimating best-corrected visual acuity from fundus photographs in eyes with diabetic macular edema

Reference 25

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Observation 042adcda-2067-4241-8db5-c661fa93a108 · outbound

This paper cites Dataset shift in machine learning.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Dataset shift in machine learning

Reference 26

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This paper cites Uncertainty quantification and deep ensembles.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Uncertainty quantification and deep ensembles

Reference 27

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Observation e712f024-3e75-433f-ac04-47a1c9498b85 · outbound

This paper cites Fundus image quality assessment: survey, challenges, and future scope.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Fundus image quality assessment: survey, challenges, and future scope

Reference 28

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This paper cites Classification with valid and adaptive coverage.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Classification with valid and adaptive coverage

Reference 29

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This paper cites Improving adaptive conformal prediction using self-supervised learning.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Improving adaptive conformal prediction using self-supervised learning

Reference 30

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Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Pac prediction sets under label shift

Reference 31

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This paper cites Variability of measurements of visual acuity in a large eye clinic.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Variability of measurements of visual acuity in a large eye clinic

Reference 32

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This paper cites Improving expert predictions with conformal prediction.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Improving expert predictions with conformal prediction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:09.854090Z

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Observation 42674f8a-9095-496c-ba01-dc4fe8135aab · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Efficientnetv2: Smaller models and faster training

Reference 34

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Observation 36d696a7-ef00-4544-b17c-4be663411f15 · outbound

This paper cites A theory of the learnable.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees A theory of the learnable

Reference 35

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This paper cites Repeatability and reliability of the visual acuity examination on logmar etdrs and snellen chart.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Repeatability and reliability of the visual acuity examination on logmar etdrs and snellen chart

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:09.818178Z

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Observation daf769af-1cf0-4719-87e8-4c932230aacb · outbound

This paper cites Conditional validity of inductive conformal predictors.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Conditional validity of inductive conformal predictors

Reference 37

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This paper cites Algorithmic learning in a random world, volume 29.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Algorithmic learning in a random world, volume 29

Reference 38

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This paper cites Automorph: automated retinal vascular morphology quantification via a deep learning pipeline.

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees Automorph: automated retinal vascular morphology quantification via a deep learning pipeline

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:09.786272Z

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Observation 80b06ab6-a4aa-47a4-befe-c6cc2f1d87c9 · outbound

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

Fundus Image-based Visual Acuity Assessment with PAC-Guarantees A foundation model for generalizable disease detection from retinal images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:09.773271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.