Paper Citation Record · LEDGER

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2509.08618.

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

pith.paper-citation-record.v1
2509.08618 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:21:09.090841Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation f9bb0392-3d2d-445a-b581-da2bcd98e46d · outbound

This paper cites Segment anything in optical coherence tomography: Sam 2 for volumetric segmentation of retinal biomarkers,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Segment anything in optical coherence tomography: Sam 2 for volumetric segmentation of retinal biomarkers,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.951543Z

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Observation 97cb7ec4-dcdb-4be4-866a-6dde16fc3425 · outbound

This paper cites Adapting the segment anything model for multi-modal retinal anomaly detection and localization,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Adapting the segment anything model for multi-modal retinal anomaly detection and localization,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.956143Z

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Observation bcacc03a-efbb-41fd-a1cb-a45ea7db5fae · outbound

This paper cites A generic approach to pathological lung segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging A generic approach to pathological lung segmentation,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.959935Z

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Observation c820ebea-0645-4701-9dcb-7ccd99e57387 · outbound

This paper cites Oct angiography findings in acute central serous chorioretinopathy,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Oct angiography findings in acute central serous chorioretinopathy,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.963852Z

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Observation 250d4f0c-2278-4209-83cd-7b6d61202a46 · outbound

This paper cites Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.967465Z

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Observation 3bcffa4e-6b57-43f6-92af-577b3d001f91 · outbound

This paper cites A comprehensive survey of foundation models in medicine,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging A comprehensive survey of foundation models in medicine,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.971472Z

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Observation 25637a9d-3cd1-448e-8df2-4b8b1682fd54 · outbound

This paper cites A review on medical image segmentation: Datasets, technical models, challenges and solutions,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging A review on medical image segmentation: Datasets, technical models, challenges and solutions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.975259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc878d7e-98a2-4791-b3ca-c0ce34b714b7 · outbound

This paper cites Medclip-sam: Bridging text and image towards universal medical image segmenta- tion,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Medclip-sam: Bridging text and image towards universal medical image segmenta- tion,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.978886Z

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Observation f20a7e81-40c5-4971-be01-187e0003fa8f · outbound

This paper cites Segment anything in medical images,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Segment anything in medical images,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.982148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba956cf2-49e7-4fec-a1e9-fe811b981425 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.985311Z

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Observation 46ae7098-c335-4f86-be00-44a4a4b43f83 · outbound

This paper cites Clip-art: Contrastive pre-training for fine- grained art classification,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Clip-art: Contrastive pre-training for fine- grained art classification,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.988634Z

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Observation 274b1e90-326e-4669-bb85-e2e077121f58 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging U-net: Convolutional networks for biomedical image segmentation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.992040Z

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Observation f7afdd39-1f6a-4777-8e9a-00fdf246c70c · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Unet++: A nested u-net architecture for medical image segmentation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.995647Z

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Observation abb4a4c4-819c-4903-992e-d229538147b7 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Attention U-Net: Learning Where to Look for the Pancreas

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:08.998938Z

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Observation 863a2b6b-0ddc-48e9-af93-8d2f4bc8f4fb · outbound

This paper cites Advances in retinal microa- neurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Advances in retinal microa- neurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.002865Z

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Observation 8fd56858-feef-4400-8271-23c5535478bc · outbound

This paper cites Universeg: Universal medical image segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Universeg: Universal medical image segmentation,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.006375Z

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Observation ef5f08df-0d40-4e1e-a960-4253c859d1ac · outbound

This paper cites Unified medical image segmentation by learning from uncertainty in an end- to-end manner,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Unified medical image segmentation by learning from uncertainty in an end- to-end manner,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.009762Z

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Observation eba8481c-4be6-4bd4-8dda-b1c24f224ed9 · outbound

This paper cites Osam-fundus: A training-free, one- shot segmentation framework for optic disc and cup in fundus images,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Osam-fundus: A training-free, one- shot segmentation framework for optic disc and cup in fundus images,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.013261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0083e6f-160e-4237-b777-17fdad8d3e26 · outbound

This paper cites Exploring the transfer learning capabilities of clip in domain generalization for diabetic retinopathy,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Exploring the transfer learning capabilities of clip in domain generalization for diabetic retinopathy,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.016848Z

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Observation 7295ef5f-306d-42a0-8c8f-31f43b602647 · outbound

This paper cites Chatffa: interactive visual question answering on fundus fluo- rescein angiography image using chatgpt,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Chatffa: interactive visual question answering on fundus fluo- rescein angiography image using chatgpt,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.020605Z

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Observation 2225f961-355f-4caa-bd53-2bfb815b0880 · outbound

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

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging A foundation model for generalizable disease detection from retinal images,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.024179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2699fac3-a7f0-4d26-8ccc-119ef7a4c469 · outbound

This paper cites Segment anything,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Segment anything,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.027545Z

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Observation 15b66f92-1ce4-4b33-8d8d-ccebbe2e64c8 · outbound

This paper cites A review of the segment anything model (sam) for medi- cal image analysis: Accomplishments and perspectives,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging A review of the segment anything model (sam) for medi- cal image analysis: Accomplishments and perspectives,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.030942Z

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Unavailable: canonical work link unavailable.

Observation 08710715-437f-49e4-a0b7-eace4c75c0ca · outbound

This paper cites Research on medical image segmentation based on sam and its future prospects,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Research on medical image segmentation based on sam and its future prospects,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.034304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77562fb8-610a-47b8-963e-421abbc30ecf · outbound

This paper cites Fapn: Feature-aligned pyramid network for dense image prediction,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Fapn: Feature-aligned pyramid network for dense image prediction,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.037662Z

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Observation 06da3e16-b250-4898-a6f6-d48cc8690113 · outbound

This paper cites Unimed-clip: Towards a unified image-text pretraining paradigm for diverse medical imaging modalities,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Unimed-clip: Towards a unified image-text pretraining paradigm for diverse medical imaging modalities,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.041122Z

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Observation 800b8865-17a1-4496-be60-3acf48fab106 · outbound

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

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Origa-light: An online retinal fundus image database for glaucoma analysis and research,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.044654Z

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Observation ce0a427c-583f-4e3a-bd5e-2d9414edf602 · outbound

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

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.048593Z

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Observation 159f7858-95e0-4263-9868-816eaa2b8444 · outbound

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

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Teleophta: Machine learning and image processing methods for teleophthalmology,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.052110Z

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Observation dc45fea8-1123-48a8-b1fd-196fdc1b3a4d · outbound

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

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Idrid: Diabetic retinopathy–segmentation and grading challenge,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.055667Z

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Unavailable: canonical work link unavailable.

Observation 84349a2a-3e1b-4791-9e21-39a383904c16 · outbound

This paper cites Diagnostic as- sessment of deep learning algorithms for diabetic retinopathy screening,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Diagnostic as- sessment of deep learning algorithms for diabetic retinopathy screening,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.059155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43c77d5d-2190-43ea-a4cd-22855d0e9a51 · outbound

This paper cites Learn to segment retinal lesions and beyond,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Learn to segment retinal lesions and beyond,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.062355Z

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Unavailable: canonical work link unavailable.

Observation 84313f69-27b5-404e-99e6-d87e1d8ca23c · outbound

This paper cites Retouch: The retinal oct fluid detection and segmentation benchmark and challenge,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Retouch: The retinal oct fluid detection and segmentation benchmark and challenge,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.065863Z

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Observation 29560c81-7a67-42bc-bf65-1d6cf9d905be · outbound

This paper cites Amd-sd: An optical coherence tomography image dataset for wet amd lesions segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Amd-sd: An optical coherence tomography image dataset for wet amd lesions segmentation,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.069359Z

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Unavailable: canonical work link unavailable.

Observation d7fc3ff5-14fe-4583-ab8d-bb5226b92c6c · outbound

This paper cites Oimhs: An optical coherence tomography image dataset based on macular hole manual segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Oimhs: An optical coherence tomography image dataset based on macular hole manual segmentation,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.072779Z

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Unavailable: canonical work link unavailable.

Observation 97297d9d-d572-483a-b9ec-6f23814ed8ab · outbound

This paper cites Aroi: Annotated retinal oct images database,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Aroi: Annotated retinal oct images database,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.076783Z

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Unavailable: canonical work link unavailable.

Observation 8dce0440-a70b-4402-9974-71cbf97ec35b · outbound

This paper cites Deep learning based automated detection of intraretinal cystoid fluid,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Deep learning based automated detection of intraretinal cystoid fluid,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.080477Z

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Unavailable: canonical work link unavailable.

Observation f1723232-6bc1-470b-95a6-d96f803bb443 · outbound

This paper cites Dual-tree complex wavelet input transform for cyst segmentation in oct images based on a deep learning framework,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Dual-tree complex wavelet input transform for cyst segmentation in oct images based on a deep learning framework,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.084127Z

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Unavailable: canonical work link unavailable.

Observation 603e834a-6cc3-438a-a092-2d44af855019 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.087489Z

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Unavailable: canonical work link unavailable.

Observation d3a08d54-1eff-4ec1-a3ad-879e11fe13b7 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T20:21:09.090841Z

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Pith citing papers

No inbound Pith citation observations are available.