Paper Citation Record · LEDGER

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.07006.

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

pith.paper-citation-record.v1
2507.07006 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:55:42.909470Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

29 of 29 outbound references displayed

verified exact2
verified fuzzy12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9932535-b6c4-47bc-a3f4-c420b54bb7be · outbound

This paper cites Inference of captions from histopatho- logical patches,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Inference of captions from histopatho- logical patches,

Reference 1

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Observation 739cf3c5-fac6-455f-a33e-610924a0e4a3 · outbound

This paper cites Artificial intelligence for digital and compu- tational pathology,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Artificial intelligence for digital and compu- tational pathology,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.438872Z

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Observation 2139d102-de3e-4115-acf9-04a56fdd76db · outbound

This paper cites PathAlign: A vision-language model for whole slide images in histopathology.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning PathAlign: A vision-language model for whole slide images in histopathology

Reference 3

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Observation 7befc8a2-bffe-4772-b5eb-76a5584d584c · outbound

This paper cites Attention-based deep multiple instance learning,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Attention-based deep multiple instance learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.422584Z

Source-reported events for the cited work

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Observation 728e977a-1ac4-40dd-ad78-842a2b4fe611 · outbound

This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Transmil: Transformer based correlated multiple instance learning for whole slide image classification,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.403249Z

Source-reported events for the cited work

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Observation 6f2a7822-8f9b-4d26-8dac-0a14827ebf07 · outbound

This paper cites A survey on graph-based deep learning for computational histopathology,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning A survey on graph-based deep learning for computational histopathology,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.384796Z

Source-reported events for the cited work

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Observation cf583242-523d-4b31-a16a-a31f1ad19426 · outbound

This paper cites Enhanced descriptive captioning model for histopathological patches,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Enhanced descriptive captioning model for histopathological patches,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.368148Z

Source-reported events for the cited work

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Observation 7b837ee8-7723-49bf-8f44-306d52212c12 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning LLaMA: Open and Efficient Foundation Language Models

Reference 8

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Source-reported events for the cited work

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Observation 02665d5e-0043-4810-b3c6-686424d71821 · outbound

This paper cites Clinicalt5: A generative language model for clinical text,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Clinicalt5: A generative language model for clinical text,

Reference 9

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Source-reported events for the cited work

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Observation b8c755d5-f623-452f-9812-c7db0bba8579 · outbound

This paper cites Biogpt: generative pre-trained transformer for biomedical text genera- tion and mining,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Biogpt: generative pre-trained transformer for biomedical text genera- tion and mining,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.787842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6efab8ef-5069-41e8-84b1-25c65161e0c8 · outbound

This paper cites A generalist vision–language foundation model for diverse biomedical tasks,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning A generalist vision–language foundation model for diverse biomedical tasks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.326042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 65261c9b-15de-480b-a638-794e88d896c9 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Imagenet large scale visual recognition challenge,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.799479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e266c0f8-8819-4563-a81e-561eb8c19ea5 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Learning transferable visual models from natural language supervision,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.807114Z

Source-reported events for the cited work

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Observation 48256ba8-a2cf-45c3-8012-278cf66882fb · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.812086Z

Source-reported events for the cited work

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Observation 495defc2-7de4-4268-b6b0-fb972fd55273 · outbound

This paper cites PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:55:43.039262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7dc63f0-fc8b-47cf-8f76-817d84553ec2 · outbound

This paper cites Dual-stream multiple instance learn- ing network for whole slide image classification with self-supervised contrastive learning,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Dual-stream multiple instance learn- ing network for whole slide image classification with self-supervised contrastive learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.827274Z

Source-reported events for the cited work

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Observation b4a3ca2f-297d-4129-bd08-f09cd1b4717c · outbound

This paper cites Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.833688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97216124-38e4-4580-af9d-2cfcef796500 · outbound

This paper cites Visual language pretrained multiple instance zero-shot transfer for histopathology images,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Visual language pretrained multiple instance zero-shot transfer for histopathology images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.244937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5109a3e4-46d6-4c05-8712-f734fc9d1172 · outbound

This paper cites CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.848632Z

Source-reported events for the cited work

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Observation 2658958b-a776-418b-bdd7-870f4b9e42d3 · outbound

This paper cites Large language models in healthcare and medical domain: A review,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Large language models in healthcare and medical domain: A review,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.221309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02e84621-ff0b-464c-b8bd-93d432a6204c · outbound

This paper cites What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:55:42.992013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1de277b8-78c8-49df-8c52-4116cf10e648 · outbound

This paper cites Patch-based convolutional neural network for whole slide tissue image classification,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Patch-based convolutional neural network for whole slide tissue image classification,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.197120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 68dadbd5-0921-47af-b719-846e4ebc45d4 · outbound

This paper cites Unsupervised deep embedding for clustering analysis,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Unsupervised deep embedding for clustering analysis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.173062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 424ea3b9-5158-4c0b-b301-59e52794ab7d · outbound

This paper cites Attention is all you need,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Attention is all you need,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.879763Z

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Observation 3b64879b-1ced-4796-877f-520141799fea · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Categorical Reparameterization with Gumbel-Softmax

Reference 25

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Observation 7e0bb6d2-6446-4a3e-9a87-5c3cbc466655 · outbound

This paper cites Graph attention networks,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Graph attention networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:42.893311Z

Source-reported events for the cited work

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Observation 18ce9874-2ea8-4468-a38a-e9a3918433be · outbound

This paper cites Rectifier nonlinearities improve neural network acoustic models,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning Rectifier nonlinearities improve neural network acoustic models,

Reference 27

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Observation 54c78102-d8bc-4517-8bc0-9e5faaa68d60 · outbound

This paper cites A dataset for breast cancer histopathological image classification,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning A dataset for breast cancer histopathological image classification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.122386Z

Source-reported events for the cited work

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Observation be848d36-dd34-4587-b09c-cf81813c94c5 · outbound

This paper cites A survey of evaluation metrics used for nlg systems,.

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning A survey of evaluation metrics used for nlg systems,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:55:43.099400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.