Paper Citation Record · LEDGER

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification

As of 17 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.02825.

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

pith.paper-citation-record.v1
2412.02825 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:07:52.754748Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

38 of 38 outbound references displayed

verified exact2
verified fuzzy29
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c130018-d5be-4112-82c1-353565b62927 · outbound

This paper cites Retinal diseases and vision 2020.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Retinal diseases and vision 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:59.010070Z

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.

Observation e1192902-7cd7-476f-ba1b-166a4af84157 · outbound

This paper cites Beyond mobilenet: An improved mobilenet for retinal diseases.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Beyond mobilenet: An improved mobilenet for retinal diseases

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.864740Z

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.

Observation fc2236ef-f2eb-422c-b1d1-4e41b073a5ab · outbound

This paper cites Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.694745Z

Source-reported events for the cited work

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Observation 82f99472-45bb-4ec9-a399-39a1aae23bda · outbound

This paper cites RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection

Reference 4

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Source-reported events for the cited work

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Observation 1235a39f-20c1-4fce-ae86-d20112be6685 · outbound

This paper cites Diabetic retinopathy detection through integration of deep learning classification framework.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Diabetic retinopathy detection through integration of deep learning classification framework

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.534742Z

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

Observation 04744253-941f-4890-9385-361939a13b1f · outbound

This paper cites Dumitrascu, and Yalin Wang.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Dumitrascu, and Yalin Wang

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.364748Z

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.

Observation f5c15455-358e-468f-ae83-6e608eabbaba · outbound

This paper cites V o and Abhishek Verma.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification V o and Abhishek Verma

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.184728Z

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.

Observation c57f5563-613e-4bf2-9eb5-a52f560245f7 · outbound

This paper cites Image processing and classification in diabetic retinopathy: A review.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Image processing and classification in diabetic retinopathy: A review

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:58.024753Z

Source-reported events for the cited work

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Observation e0f16a82-06c6-4c4e-ac68-b453f55b1fa8 · outbound

This paper cites Fast key points detection and matching for tree- structured images.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Fast key points detection and matching for tree- structured images

Reference 9

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Source-reported events for the cited work

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Observation c834f973-fcdc-4e93-b75b-5347229a0483 · outbound

This paper cites Green: a graph residual re-ranking network for grading diabetic retinopathy.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Green: a graph residual re-ranking network for grading diabetic retinopathy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:57.714900Z

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.

Observation 9a0111e6-77f8-43cb-800b-9fcb8a63d038 · outbound

This paper cites Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:57.544744Z

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.

Observation 8d907a42-75cd-4b11-97cc-fa5d6b6947da · outbound

This paper cites Automated retinal imaging analysis for alzheimers disease screening.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Automated retinal imaging analysis for alzheimers disease screening

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:57.374750Z

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.

Observation 6061eb2b-4704-46d0-a942-781d79a2c635 · outbound

This paper cites Mayo Clinic Proceedings: Digital Health, 2024.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Mayo Clinic Proceedings: Digital Health, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:57.194751Z

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.

Observation 84b7b397-e977-4ee8-b554-93122f79464e · outbound

This paper cites Robust pca with lw, and l2, 1 norms: A novel method for low-quality retinal image enhancement.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Robust pca with lw, and l2, 1 norms: A novel method for low-quality retinal image enhancement

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:57.020985Z

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.

Observation 55c29c01-52b7-490b-b75c-dfa207b2c88f · outbound

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

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification A deep learning system for detecting diabetic retinopathy across the disease spectrum

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:56.854743Z

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.

Observation 2b1a58da-897b-4fa4-92c5-c188dc093486 · outbound

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

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:56.720821Z

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.

Observation f1dec730-1d0c-429c-b05a-6df17b6947d3 · outbound

This paper cites Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T23:07:51.774743Z

Source-reported events for the cited work

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Observation dc0c3a41-aca4-4b5d-be81-2bcb36428bfc · outbound

This paper cites an unresolved cited work.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:07:56.571748Z

Source-reported events for the cited work

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Observation 0854fca2-ba83-447c-a479-1a2ca6e73b8d · outbound

This paper cites Zoom-in-net: Deep mining lesions for diabetic retinopathy detection.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Zoom-in-net: Deep mining lesions for diabetic retinopathy detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:56.424156Z

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.

Observation dc33d9dc-4ca5-4121-b4fc-7d64d9b1b76a · outbound

This paper cites Collaborative learning of semi-supervised segmentation and classification for medical images.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Collaborative learning of semi-supervised segmentation and classification for medical images

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:56.264867Z

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.

Observation cd76dfdb-4aa8-4e40-b0fa-4cc5f4a7a672 · outbound

This paper cites Chen, and Jian Wu.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Chen, and Jian Wu

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:56.114817Z

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.

Observation ddb0bb99-d42c-423a-8fef-6ddf0152f4ea · outbound

This paper cites Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.944746Z

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.

Observation cf25fc1e-acd2-4179-8f2f-f26191407833 · outbound

This paper cites Self-supervised equivariant regularization reconciles multiple instance learning: Joint referable diabetic retinopathy classification and lesion segmentation.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Self-supervised equivariant regularization reconciles multiple instance learning: Joint referable diabetic retinopathy classification and lesion segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.779976Z

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.

Observation 2c12f19d-7919-4e5d-98a4-a940939bf49f · outbound

This paper cites Transformers in vision: A survey.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Transformers in vision: A survey

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.584743Z

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.

Observation 63342e0b-e9e5-43f7-962a-8a53e41ceadd · outbound

This paper cites Otre: Where optimal transport guided unpaired image-to-image translation meets regularization by enhancing.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Otre: Where optimal transport guided unpaired image-to-image translation meets regularization by enhancing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.424744Z

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.

Observation 6fcf919e-4886-4ec8-aedb-bf0922ad6591 · outbound

This paper cites Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:07:53.464746Z

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.

Observation 3ca0b833-7dc8-475a-8a11-a7a3e5d6e81e · outbound

This paper cites Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:07:53.243922Z

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.

Observation e21bb75b-323a-4b48-b6b4-83aa10e7e322 · outbound

This paper cites Using mri-specific data augmentation to enhance the segmentation of right ventricle in multi-disease, multi-center and multi-view cardiac mri.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Using mri-specific data augmentation to enhance the segmentation of right ventricle in multi-disease, multi-center and multi-view cardiac mri

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.244748Z

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.

Observation cf65566f-eb28-4131-9ea1-02fa62211269 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:55.094742Z

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.

Observation b0697f7e-e44b-493e-a757-4328ea724a7d · outbound

This paper cites Efficient object localization using convolutional networks.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Efficient object localization using convolutional networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:54.948092Z

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.

Observation b916ecdb-43aa-4cbf-acb9-02866627f3a9 · outbound

This paper cites Cbam: Convolutional block attention module.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Cbam: Convolutional block attention module

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T23:07:52.424737Z

Source-reported events for the cited work

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Observation 9f7a6aa0-816e-4a9f-b13b-f03addba8472 · outbound

This paper cites Rethinking channel dimensions for efficient model design.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Rethinking channel dimensions for efficient model design

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:54.739794Z

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.

Observation cf79dccc-4e2f-4e9b-8238-48bd9b896f7d · outbound

This paper cites Squeeze-and-excitation networks.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Squeeze-and-excitation networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T23:07:52.524959Z

Source-reported events for the cited work

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Observation 00ea7bb6-7750-42c9-9c19-04552a1e6237 · outbound

This paper cites Lesion-aware transformers for diabetic retinopathy grading.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Lesion-aware transformers for diabetic retinopathy grading

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:54.474743Z

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.

Observation a5659ed9-9c0b-4efe-af61-833a7414bc9c · outbound

This paper cites Satformer: Saliency-guided abnormality-aware transformer for retinal disease classification in fundus image.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Satformer: Saliency-guided abnormality-aware transformer for retinal disease classification in fundus image

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:54.302902Z

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.

Observation 893b099d-dafc-45e7-839e-98f3708150f1 · outbound

This paper cites A convnet for the 2020s.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification A convnet for the 2020s

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:54.134891Z

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.

Observation cd9f2617-5304-40ab-8c9c-90c91e6754cc · outbound

This paper cites Mil-vt: Multiple instance learning enhanced vision transformer for fundus image classification.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification Mil-vt: Multiple instance learning enhanced vision transformer for fundus image classification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:07:53.944940Z

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.

Observation fdb31e24-705b-4bea-a14e-d9a8929eabf0 · outbound

This paper cites AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights.

Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T23:07:52.754748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.