Paper Citation Record · LEDGER

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models

As of 20 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2411.15232.

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

pith.paper-citation-record.v1
2411.15232 v2

Coverage vector

measured 63 of 63 reference resolution

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measured 64 of 64 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation ed09910c-ca91-4d1b-99e5-0931e84cc3a9 · outbound

This paper cites GPT-4 Technical Report.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models GPT-4 Technical Report

Reference 1

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Observation 9dc0e63d-588d-4f44-a9db-81b51973e4a7 · outbound

This paper cites Dataset of breast ultrasound images.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Dataset of breast ultrasound images

Reference 2

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Observation fcdb72d8-7b3b-45c4-bd46-f78010dd2f11 · outbound

This paper cites Xcoop: Explainable prompt learning for computer-aided di- agnosis via concept-guided context optimization.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Xcoop: Explainable prompt learning for computer-aided di- agnosis via concept-guided context optimization

Reference 3

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Observation 44712d8b-a15d-4df0-bd4f-8d0896eff968 · outbound

This paper cites Making the most of text semantics to improve biomedical vision–language processing.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Making the most of text semantics to improve biomedical vision–language processing

Reference 4

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Observation ef060e51-c2eb-4b74-b004-9866ce47db08 · outbound

This paper cites Borkowski, Marilyn M.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Borkowski, Marilyn M

Reference 5

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Observation fe56cb3c-208c-4c7a-975f-4242945006f1 · outbound

This paper cites Domain-controlled prompt learning.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Domain-controlled prompt learning

Reference 6

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Observation 19f4a63b-3d5a-49d2-bdff-2ceede1b1ec2 · outbound

This paper cites Knee osteoarthritis severity grading dataset,.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Knee osteoarthritis severity grading dataset,

Reference 7

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Observation d2fee4e5-83a2-4efd-ab3d-0434494b15be · outbound

This paper cites gscorecam: What objects is clip looking at? In Proceedings of the Asian Conference on Computer Vision , pages 1959– 1975, 2022.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models gscorecam: What objects is clip looking at? In Proceedings of the Asian Conference on Computer Vision , pages 1959– 1975, 2022

Reference 8

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Observation ac0717ef-c12b-471f-babb-0361e3947328 · outbound

This paper cites brain tumor dataset, 2017.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models brain tumor dataset, 2017

Reference 9

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Observation d39db563-e425-4f20-9eb8-1603c179d41f · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 10

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Observation 71addbd1-270c-4157-8f6f-667f989c344a · outbound

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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Unresolved cited work

Reference 11

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Observation 405856ac-d586-4484-ba50-933c68cddcc5 · outbound

This paper cites BCN20000: Dermoscopic Lesions in the Wild.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models BCN20000: Dermoscopic Lesions in the Wild

Reference 12

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Observation 5ae2a1a3-b2c1-4ecf-a679-6ebfd09606b3 · outbound

This paper cites Cleft: Language-image contrastive learning with efficient large language model and prompt fine-tuning.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Cleft: Language-image contrastive learning with efficient large language model and prompt fine-tuning

Reference 13

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Observation 80e2ac09-fb65-4547-b82e-2ae6dd0ccabc · outbound

This paper cites Does clip benefit visual question answering in the medical domain as much as it does in the general domain?, 2021.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Does clip benefit visual question answering in the medical domain as much as it does in the general domain?, 2021

Reference 14

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Observation b6bfa79a-a107-4eb3-aa86-5ce25ab4255c · outbound

This paper cites Aligning medical images with general knowl- edge from large language models.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Aligning medical images with general knowl- edge from large language models

Reference 15

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Observation fcffca70-3690-468a-af47-1d44197ff716 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Clip-adapter: Better vision-language models with feature adapters

Reference 16

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Observation ceb70338-c4c9-41bd-b0df-3f45f2797d05 · outbound

This paper cites Potential of gpt-4 for de- tecting errors in radiology reports: Implications for reporting accuracy.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Potential of gpt-4 for de- tecting errors in radiology reports: Implications for reporting accuracy

Reference 17

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Observation 6fad88db-0186-4512-ac5a-74c4a95f834c · outbound

This paper cites Parameter-efficient transfer learning for nlp.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Parameter-efficient transfer learning for nlp

Reference 18

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Observation 34dc4096-2c12-4ebe-b10c-fd066a418b1b · outbound

This paper cites Lp++: A surprisingly strong linear probe for few-shot clip.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Lp++: A surprisingly strong linear probe for few-shot clip

Reference 19

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Observation 2ca8546b-d8ce-4c6e-80af-48f91d4f0ea6 · outbound

This paper cites Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from ct- radiography.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from ct- radiography

Reference 20

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Observation 5120db6a-f001-49ff-8d70-721bd80b3151 · outbound

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

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 21

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Observation 16344d51-c40b-42fb-bdc2-a0475ab3fb42 · outbound

This paper cites Multi-class texture anal- ysis in colorectal cancer histology.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Multi-class texture anal- ysis in colorectal cancer histology

Reference 22

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Observation f45083a9-389e-43d6-af3a-18e20c06ba5b · outbound

This paper cites Kermany, Michael Goldbaum, et al.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Kermany, Michael Goldbaum, et al

Reference 23

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This paper cites Maple: 9 Multi-modal prompt learning.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Maple: 9 Multi-modal prompt learning

Reference 24

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Observation f15c24ae-e2e4-468a-b0bd-b8d700785a94 · outbound

This paper cites Maple: Multi-modal prompt learning.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Maple: Multi-modal prompt learning

Reference 25

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Observation a6479503-1a7b-4000-9a62-95f031de8e12 · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Self-regulating prompts: Foundational model adaptation without forgetting

Reference 26

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Observation c367b3aa-d66b-425f-91f5-46a4b2ac7e46 · outbound

This paper cites Learning to Prompt with Text Only Supervision for Vision-Language Models.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Learning to Prompt with Text Only Supervision for Vision-Language Models

Reference 27

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This paper cites Medclip-sam: Bridging text and image towards universal medical image segmentation.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Medclip-sam: Bridging text and image towards universal medical image segmentation

Reference 28

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Observation 0d08898b-3a99-4006-946e-f28599c6393e · outbound

This paper cites MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

Reference 29

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Observation 29c8f5ce-8f1c-462d-b755-b2efffc86516 · outbound

This paper cites Evaluation of reliability, repeatability, ro- bustness, and confidence of gpt-3.5 and gpt-4 on a radiology board–style examination.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Evaluation of reliability, repeatability, ro- bustness, and confidence of gpt-3.5 and gpt-4 on a radiology board–style examination

Reference 30

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This paper cites Automatic no- reference quality assessment for retinal fundus images using vessel segmentation, 2013.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Automatic no- reference quality assessment for retinal fundus images using vessel segmentation, 2013

Reference 31

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Observation 9a5224e3-18bd-48f9-89be-fe16fd31aaf1 · outbound

This paper cites A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis

Reference 32

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This paper cites Segment anything in medical images.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Segment anything in medical images

Reference 33

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This paper cites Brain tumor mri dataset, 2021.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Brain tumor mri dataset, 2021

Reference 34

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Observation 2827826d-6c71-42fa-98ec-22a5c3a9b0b4 · outbound

This paper cites Kvasir: A multi-class image dataset for com- puter aided gastrointestinal disease detection.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Kvasir: A multi-class image dataset for com- puter aided gastrointestinal disease detection

Reference 35

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This paper cites Indian diabetic retinopathy image dataset (idrid), 2018.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Indian diabetic retinopathy image dataset (idrid), 2018

Reference 36

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Observation 7185b9a8-1bcc-4d93-b980-f9661c82988f · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Learning transferable visual models from natural language supervi- sion

Reference 37

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This paper cites A closer look at the few-shot adaptation of large vision-language models.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models A closer look at the few-shot adaptation of large vision-language models

Reference 38

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BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Tahir, Muhammad E.H

Reference 39

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This paper cites The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions

Reference 40

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This paper cites The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions

Reference 41

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This paper cites Advances in medical image seg- mentation: A comprehensive review of traditional, deep learning and hybrid approaches.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Advances in medical image seg- mentation: A comprehensive review of traditional, deep learning and hybrid approaches

Reference 42

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This paper cites Visual- language prompt tuning with knowledge-guided context op- timization, 2023.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Visual- language prompt tuning with knowledge-guided context op- timization, 2023

Reference 43

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Observation acbe8bd8-8ac0-4ed5-ab92-8000bd5b5ec4 · outbound

This paper cites Visual- language prompt tuning with knowledge-guided context op- timization.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Visual- language prompt tuning with knowledge-guided context op- timization

Reference 44

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This paper cites Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices

Reference 45

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This paper cites Prefer: Prompt ensemble learning via feedback-reflect-refine.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Prefer: Prompt ensemble learning via feedback-reflect-refine

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.279378Z

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Observation 71b9972d-50e2-4925-9c50-005651153976 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 47

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This paper cites Lungren, Tristan Naumann, Sheng Wang, and Hoifung Poon.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Lungren, Tristan Naumann, Sheng Wang, and Hoifung Poon

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.259877Z

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Observation 8c8d619a-9af2-42e9-ba3b-9bb4f8d31dd2 · outbound

This paper cites CLIP in Medical Imaging: A Survey.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models CLIP in Medical Imaging: A Survey

Reference 49

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This paper cites Conditional prompt learning for vision-language models.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Conditional prompt learning for vision-language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.241598Z

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Observation 12a29ed9-5952-496a-8c6e-74d8374c24e2 · outbound

This paper cites Learning to prompt for vision-language models.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Learning to prompt for vision-language models

Reference 51

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Source-reported events for the cited work

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Observation 7cd2f9c1-228b-43e5-ba6a-7ec8422b804b · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Prompt-aligned gradient for prompt tuning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.211590Z

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Observation 3c904205-fe18-4728-9897-3284d7e6c94e · outbound

This paper cites Prompt-aligned gradient for prompt tuning, 2024.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Prompt-aligned gradient for prompt tuning, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.194005Z

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Observation d9123879-8771-468f-b8e9-118416f124b8 · outbound

This paper cites Each dataset is described in terms of its imaging modality, target organ(s), number of classes, and dataset splits (train/validation/test).

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Each dataset is described in terms of its imaging modality, target organ(s), number of classes, and dataset splits (train/validation/test)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.177106Z

Source-reported events for the cited work

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Observation d139fd1a-cdeb-4e23-9a9b-d592a80c03f0 · outbound

This paper cites It underscores BiomedCoOp’s robustness in adapting to limited data.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models It underscores BiomedCoOp’s robustness in adapting to limited data

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.158507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e4711f6-2bea-4b23-bb0c-c246e4d05d9a · outbound

This paper cites A photo of [CLASS].

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models A photo of [CLASS]

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.137573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1409ff9f-6daa-40e9-bd59-3d3c8e5c20dc · outbound

This paper cites A shorter context length, such as 4, achieves a better balance between base and novel accuracy, resulting in a higher har- monic mean (HM) score.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models A shorter context length, such as 4, achieves a better balance between base and novel accuracy, resulting in a higher har- monic mean (HM) score

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.119752Z

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Observation c7abd5b4-bc26-4854-8508-ac63f757af8f · outbound

This paper cites Figure S2 illus- trates the impact of increasing the selection threshold ( ζs) for the absolute value of the modified z-score to allow more prompts generated by the LLM to be used.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Figure S2 illus- trates the impact of increasing the selection threshold ( ζs) for the absolute value of the modified z-score to allow more prompts generated by the LLM to be used

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.098981Z

Source-reported events for the cited work

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Observation 2c095f47-2174-4319-a187-0d10ada21465 · outbound

This paper cites To verify the effect of prompt selection for SCCM, we compare the model per- formance with and without prompt selection.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models To verify the effect of prompt selection for SCCM, we compare the model per- formance with and without prompt selection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:55.077191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baf3c2bd-3840-4fd7-bdfd-d0bfcb5ab8f5 · outbound

This paper cites We also compare with zero- shot methods (in blue).

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models We also compare with zero- shot methods (in blue)

Reference 60

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aed8a65f-8f27-46dc-87c3-30e90a5ae40f · outbound

This paper cites an unresolved cited work.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:15:55.040371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8c415c8a-fb2b-4ba0-97cf-4cc6eb6dfa92 · outbound

This paper cites an unresolved cited work.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:15:55.021807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3f83676-664a-4677-9770-a9014c985ded · outbound

This paper cites The image of a normal brain on MRI shows a clear differentiation between different brain regions with no disruptions.

BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models The image of a normal brain on MRI shows a clear differentiation between different brain regions with no disruptions

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T15:15:55.002755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation df1cf5b4-669b-405d-9ecf-db0404f80320 · inbound

Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology cites this paper.

Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:56:23.296718Z

Source-reported events for the cited work

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