Paper Citation Record · LEDGER

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.01788.

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

pith.paper-citation-record.v1
2507.01788 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:48:06.271519Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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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

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation b8590585-bd2b-42b6-bcf5-e2d0ab33e881 · outbound

This paper cites On the opportunities and risks of foundation models,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging On the opportunities and risks of foundation models,

Reference 1

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Observation e82de7a8-9f9f-4596-97c4-c419e026fa51 · outbound

This paper cites Gpt-4 technical report,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Gpt-4 technical report,

Reference 2

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Observation 6b2e7be4-1945-47eb-b470-00002d852414 · outbound

This paper cites ChatGPT goes to law school,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging ChatGPT goes to law school,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.544313Z

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Observation e204a648-03f0-4fcd-bf81-0dd46e627ebe · outbound

This paper cites ProteinBERT: a universal deep- learning model of protein sequence and function,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging ProteinBERT: a universal deep- learning model of protein sequence and function,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.534886Z

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Observation 99e11830-2207-4a17-acd9-a03b41c76a7b · outbound

This paper cites Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models,

Reference 5

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Observation e4d9eaea-185d-43db-a97a-32bbe34008cd · outbound

This paper cites Attention is all you need,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Attention is all you need,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.516834Z

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Observation e68378a1-50a6-4fbe-92bf-8908865448a0 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 7

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Observation 2c20c33a-3b11-478f-a665-f617ce33976a · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Bert: Pre-training of deep bidirectional trans- formers for language understanding,

Reference 8

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This paper cites Transformers in medical imaging: A survey,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Transformers in medical imaging: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.493044Z

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Observation 1ff4674a-63c2-48f1-8475-8c1417a39f86 · outbound

This paper cites A comparative study between vision transformers and cnns in digital pathology,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging A comparative study between vision transformers and cnns in digital pathology,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.484726Z

Source-reported events for the cited work

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Observation caef6f7d-8819-4628-ac77-388d7f7ebb73 · outbound

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

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Mil-vt: Multiple instance learning enhanced vision trans- former for fundus image classification,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.475527Z

Source-reported events for the cited work

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Observation 93c4edc1-de78-4347-b9b7-b0d397b66b2f · outbound

This paper cites Med- vit: A robust vision transformer for generalized medical image classification,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Med- vit: A robust vision transformer for generalized medical image classification,

Reference 12

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This paper cites A recent survey of vision transformers for medical image segmentation,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging A recent survey of vision transformers for medical image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.458347Z

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Observation 38f040a5-fd81-47a8-ad03-2f4d6819d063 · outbound

This paper cites Comparing cnns and vits for medical image classification leveraging transfer learning,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Comparing cnns and vits for medical image classification leveraging transfer learning,

Reference 14

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Observation 0ed3c8a3-687c-4d89-bcb7-69d5c002da39 · outbound

This paper cites Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs,

Reference 15

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Observation d2408800-9548-4be7-92bf-8f18e21a73e8 · outbound

This paper cites Pmc-clip: Contrastive language-image pre-training using biomedical documents,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Pmc-clip: Contrastive language-image pre-training using biomedical documents,

Reference 16

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Observation c8cbbe66-18f8-4b59-9b4d-594fb035f5ed · outbound

This paper cites Explaining and harnessing adversarial examples,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Explaining and harnessing adversarial examples,

Reference 17

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Observation 2d80b1f1-d874-42be-a925-1f5575ca5314 · outbound

This paper cites Intriguing properties of neural networks,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Intriguing properties of neural networks,

Reference 18

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This paper cites Towards deep learn- ing models resistant to adversarial attacks,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Towards deep learn- ing models resistant to adversarial attacks,

Reference 19

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Observation d9ae84a6-12c2-45c7-89b3-7357a7e37da3 · outbound

This paper cites Survey on adversarial attack and defense for medical image analysis: Methods and challenges,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Survey on adversarial attack and defense for medical image analysis: Methods and challenges,

Reference 20

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Observation f367ca67-4dec-4617-b6f7-37134f2d2d2e · outbound

This paper cites Generalizability vs. robustness: Ad- versarial examples for medical imaging,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Generalizability vs. robustness: Ad- versarial examples for medical imaging,

Reference 21

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Observation 66bbd2e3-55fc-43b5-b4ed-cddfaa9be4c2 · outbound

This paper cites Adversarial attacks on medical machine learning,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Adversarial attacks on medical machine learning,

Reference 22

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Observation 6edd2d57-7429-456b-88fb-d8ca1c2b07a8 · outbound

This paper cites Understanding adversarial attacks on deep learning based medical image analysis systems,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Understanding adversarial attacks on deep learning based medical image analysis systems,

Reference 23

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Observation 5c8ddaf1-e292-4932-91f0-c02693fafb65 · outbound

This paper cites Adversarial attacks and adversarial robustness in computational pathology,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Adversarial attacks and adversarial robustness in computational pathology,

Reference 24

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Observation ab1cf7ac-2b95-4088-a250-dc4af2ac58e9 · outbound

This paper cites Un- derstanding robustness of transformers for image classifica- tion,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Un- derstanding robustness of transformers for image classifica- tion,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.352734Z

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Observation 7a2e4772-29b9-4664-85a3-570cc2b67765 · outbound

This paper cites Intriguing equivalence structures of the embedding space of vision transformers,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Intriguing equivalence structures of the embedding space of vision transformers,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.343626Z

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Observation 2f8df52d-5b2a-43ae-aaae-efb206723585 · outbound

This paper cites Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.333993Z

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Observation 66c8090a-1d8e-4606-b512-31911850b2d3 · outbound

This paper cites Image quality metrics: Psnr vs. ssim,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Image quality metrics: Psnr vs. ssim,

Reference 28

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Observation d02b0a57-775d-468a-af7f-310524196fcd · outbound

This paper cites Feature forwarding for efficient single image dehazing,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Feature forwarding for efficient single image dehazing,

Reference 29

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Observation f25fdd7e-4008-4677-9c08-c5c139ecf1e2 · outbound

This paper cites Understanding zero-shot adversarial robustness for large-scale models,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Understanding zero-shot adversarial robustness for large-scale models,

Reference 30

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Observation 4dc02add-cb5f-4c48-9b8b-58c28e757e26 · outbound

This paper cites Malicious path manipu- lations via exploitation of representation vulnerabilities of vision-language navigation systems,.

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging Malicious path manipu- lations via exploitation of representation vulnerabilities of vision-language navigation systems,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:06.298100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

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